Shubhankar Biswas, Author at Algodocs

How To Streamline Logistics Business Document Workflow In 2026 Using Proof of Delivery Data Extraction

Proof of Delivery Data Extraction Using AI & IDP: How to Streamline Logistics Business Document Workflow in 2026

Shubhankar Biswas / October 15, 2026

Back to Blog Table of contents On this page How To Streamline Logistics Business Document Workflow In 2026 Using Proof of Delivery Data Extraction Home › Blog › Logistics Data Extraction › Proof of Delivery Data Extraction Using AI & IDP Categories Logistics Data Extraction Tags Proof of Delivery Extraction Logistics Data Extraction API By Shubhankar Biswas Published October 15, 2026, 11:35 The global logistics industry is a $10.2 trillion USD market in 2023 and is expected to grow at a CAGR of 7.3%, and it is expected to reach $20.1 trillion USD by 2023, according to Allied Market Research data. Logistics and shipping industries deal with various types of documents on a day-to-day basis. These documents could be bills of lading, purchase orders, seaway bills, invoices, or proof of delivery documents, which are most common for successful business operations. However, the majority of these documents are either paper documents, handwritten notes, scanned image documents, or PDFs. Managing and extracting data from these documents poses great organizational challenges. Recent data from Cargoclix show that 45.5% of logistics companies still rely on paper data rather than digital document formats. Trans info’s analysis of proof of delivery shows that as much as 99% of EU international road transport still relies on paper data. Paper PODs can be lost, arrive late, carry illegible signatures, or contain errors, and disputes over them can take days or weeks to settle, which holds up invoices and payments. But the rise of AI-based data extraction tools and ML solves this proof of delivery data extraction problem by automating every step of the data extraction process. With the help of AI and IDP (Intelligent Document Processing), data extraction from POD documents and other logistics documents can be accelerated with greater accuracy and speed. In this guide, we will discuss the role of AI, IDP, and ML in proof of delivery data extraction, how it works, the challenges with POD data extraction, the best tools, and other aspects. Proof of delivery data extraction closes this gap. It combines proof of delivery OCR, machine learning, and logistics IDP (Intelligent Document Processing) to read each POD and turn it into structured data that your systems can use. This guide explains how it works, where manual methods break down, how the leading tools compare, and how to choose the right approach for your operation. TL;DR Proof of delivery data extraction automatically reads POD documents and captures fields such as delivery date, recipient, signature, shipment number and exception notes. Proof of delivery OCR only converts an image into text. IDP adds layout understanding, validation and system integration. Manual document handling is a major hurdle: 82% of respondents in a transport and logistics back office survey said it remains a significant challenge. Automation speeds up invoicing, flags damage and shortage notes early and gives finance and operations one clean data source. Five tools worth testing: Algodocs, Klippa, Affinda, Lido and Kognitos. No tool verifies signature authenticity. The original receipt remains the evidence. What Is Proof of Delivery Data Extraction and How It works? Proof of delivery data extraction is the process of using AI and intelligent document processing technologies to capture and extract key details from a POD document, such as the delivery date, recipient name, signature, and shipment reference, in a structured data format so that logistics, billing, and ERP systems can use them for other business operations. POD extraction tools can capture and extract the following fields from proof of delivery documents: Delivery date and time Recipient or signatory name Signature present or missing Tracking, PRO, order or shipment reference Delivery address or stop reference Items and quantities delivered Carrier and driver details Exception notes such as damage, shortage or refusal How Proof of Delivery Data Extraction Works Proof of delivery (POD) data extraction uses Intelligent Document Processing (IDP), which combines AI technologies to upload, read, extract, validate, and export data from delivery documents. First, the POD document is uploaded to an AI-based IDP platform. The platform combines Optical Character Recognition (OCR) with machine learning (ML) and natural language processing (NLP). If the POD is a scanned image or a scanned PDF, OCR converts it into machine-readable text. If it is a digital PDF, the system reads the embedded text layer directly. Next, the system analyzes the document layout to locate key areas such as headers, tables, line items, and signature fields. ML and NLP models then extract the required details, such as the delivery date, consignee name, shipment number, item quantities, and receiver signature, and convert them into structured fields. The extracted data is then validated. The system checks for missing values, formatting errors, and mismatches against reference data, and flags low-confidence results for human review. Finally, the structured data can be exported in formats such as CSV, Excel, or JSON. It can also be sent to other systems, such as ERP, TMS, or accounting software, through APIs. The exact steps and field names vary by IDP vendor, so check them against the specific tool you are describing. What Is a Proof of Delivery Document in the Logistics & Shipping Industry Proof of delivery is a document that confirms that the shipment was delivered to the correct recipient at the correct address at the specified time and date. You can think of a POD as the final stage in product delivery, indicating that a carrier’s responsibility to the buyer has been fulfilled. A typical proof of delivery document can contain information such as delivery date, time, tracking ID, address, recipient’s signature, etc. Template Based Proof of delivery OCR vs IDP Based Proof of delivery Data Extraction Feature Template-Based OCR IDP (Intelligent Document Processing) Core Technology Extracts text based on strict predefined spatial coordinates (Zonal OCR). Uses AI, Machine Learning, and NLP to understand context and identify data regardless of position. Document Flexibility Rigid. Fails if the document structure, spacing, or margins shift even slightly. Adaptable. Processes varied layouts, multi-page PODs, and unstructured formats seamlessly. Setup & Maintenance Requires manual drawing

Optical Character Recognition

OCR Handwriting To Text: A Complete Guide For Unlocking Valuable Data.

Shubhankar Biswas / October 9, 2026

Back to Blog Table of contents On this page OCR Handwriting To Text: A Complete Guide For Unlocking Valuable Data Home › Blog › Optical Character Recognition › OCR Handwriting To Text: A Complete Guide For Unlocking Valuable Data Categories Optical Character Recognition Tags Hand Writing Data Extraction OCR Handwriting Recognition By Shubhankar Biswas Published September 21, 2026, 10:15 Handwritten invoices, delivery notes, forms, ledgers and field reports still reach businesses every day. The information inside them is valuable, but it is stuck on paper, and someone has to type it out before software can use it. That is slow, costly and easy to get wrong. OCR handwriting to text solves this. It reads handwriting from a photo or scan and converts it into editable, searchable digital text. Writing styles vary a lot, from neat block letters to fast, joined-up cursive, so results vary too. Some tools handle neat writing well, while others are built for messy cursive or for structured business documents. In this guide, you will learn what OCR handwriting to text is, how it works, how accurate it really is, which tools are worth considering and how to choose the right one for your documents. TL;DR Summary What it is: OCR handwriting to text uses AI and machine learning to read handwriting from images or scanned PDFs and convert it into editable text or structured data such as Excel or JSON. The problem it solves: Handwritten documents cannot be searched, edited, shared or fed into business software without slow, error-prone retyping. Realistic accuracy: Neat handwriting converts well. Cursive, rushed writing, mixed languages and poor scans still need human review. Measure accuracy on your own documents, not on vendor claims. Tools covered: Algodocs and Docsumo (business document processing), Handwriting OCR (online conversion), Transkribus (historical handwriting), DeepSeek OCR (open source), Google Lens and OneNote (free options) and cloud APIs from Microsoft, Google and Amazon. How to choose: Match the tool to your document type, handwriting style, language, volume, output format, privacy needs and budget. Then test two or three tools on 30 to 50 of your own documents. What Is OCR Handwriting to Text? OCR handwriting to text is the process of using optical character recognition (OCR) and machine learning to convert handwritten content in images or scanned PDFs into editable, searchable text. The result can be plain text, a searchable PDF, a Word file, a spreadsheet row or structured data such as JSON. Several terms describe this technology, and they are often mixed up: Term What it means Best suited to OCR (optical character recognition) Reads printed text from images Typed documents, books, printed forms ICR (intelligent character recognition) OCR extended to recognize handwritten characters Handwritten digits and block letters on forms HTR (handwritten text recognition) AI models that read whole lines of handwriting, including cursive Letters, notes, manuscripts and archives IDP (intelligent document processing) OCR plus AI to classify, extract, validate and export fields Invoices, claims, delivery notes and other business documents Traditional OCR only turns a text image into an editable file. It works well on clean print but often struggles with handwriting. Modern tools add AI, machine learning and natural language processing (NLP) on top of OCR. IDP platforms go one step further by cleaning, validating and organizing the extracted data, even from irregular layouts. Why Is Handwriting Extraction Harder Than Printed Text Extraction? Printed text is uniform. The same letter looks almost identical every time, so OCR on a clean printed page is highly reliable. Handwriting is different in several ways: Letter shapes change from person to person and even within a single sentence. Letters connect, slant and overlap, so it is hard to tell where one character ends and the next begins. Ink fades, paper creases and phone photos arrive with shadows and tilted angles. There is no machine-readable font. A handwritten page is only a picture. It has no Unicode or ASCII characters behind it, so software has to interpret the shapes. A tool that performs well on a typed page can struggle badly with a page of cursive notes. That is why handwriting needs models trained specifically on handwriting, not only on printed fonts. How OCR Handwriting to Text Works Converting handwriting to text is a multi-step process. It starts with capturing an image and ends with exporting clean, usable data. Image capture: The handwritten content is provided as an image (.jpg, .png) or a scanned PDF and uploaded to the OCR system. Preprocessing: The software straightens the page, removes noise and adjusts contrast to improve image quality. Segmentation and text detection: The system finds the text areas and breaks the page into lines, words and, in some tools, individual characters. Text recognition: AI models read the detected lines and predict the most likely letters and words. Modern engines look at whole lines and surrounding context, which helps them read unclear letters the way a person would. Post-processing: The software cleans the output, fixes obvious errors and assigns confidence scores. Low-confidence words or fields can be flagged for human review. Export: The final data is exported as plain text, Word, .xls, .csv, .json, searchable PDF or another format, or sent directly to other software through an API. Note: To learn more about the basics, see our detailed guide on how OCR works. Problems With Handwritten Notes and Why OCR Handwriting to Text Helps Most handwritten documents exist only as images or scanned PDFs. They have no fixed font or layout, which makes data capture difficult. The common problems are: They cannot be searched. In a typed PDF or Word file, you can find a name, date or amount in seconds. In a handwritten image, the computer sees only pixels. They are hard to edit, copy and share. To change anything, you have to retype it. They can be hard to read. Writing style, irregular spacing and messy layouts make handwriting difficult for both people and machines. They cannot feed business systems. Invoices, delivery challans, bills of lading, customs forms and

intelligent document processing

Intelligent Document Processing Trends 2027: How IDP Is Shifting

Shubhankar Biswas / September 24, 2026

Back to Blog Table of contents On this page Intelligent Document Processing Trends 2027: How IDP Is Shifting Home › Blog › Intelligent Document Processing › Intelligent Document Processing Trends 2027: How IDP Is Shifting Categories Intelligent Document Processing Tags IDP AI Algodocs By Shubhankar Biswas Published September 24, 2026, 10:15 Intelligent Document Processing (IDP) has become more than a tool for scanning paper and pulling text out of PDFs, images, and handwritten notes. By 2027, IDP is turning into a knowledge driven, agentic, and multimodal enterprise capability. It reads documents, retrieves context, answers questions in plain language, and triggers the next step in a business process automatically. This shift is playing out across every major industry. According to MarketsandMarkets‘ Intelligent Document Processing market forecast, the global IDP market is projected to grow from USD 1.1 billion in 2022 to USD 5.2 billion by 2027, a compound annual growth rate (CAGR) of 37.5%. Longer term forecasts vary widely depending on how each research firm defines the category. Straits Research projects the market could reach USD 37.28 billion by 2033, while IMARC Group estimates USD 46.23 billion by the same year. The exact endpoint differs by analyst, but the direction is consistent: double digit growth driven by AI adoption, automation demand, and the need to process unstructured data at scale. Behind these numbers is a simple truth. Organizations want document intelligence that is accurate, governed, conversational, and integrated into end-to-end business automation, not a point tool bolted onto a legacy workflow. This article breaks down the ten trends reshaping Intelligent Document Processing heading into 2027, what the data actually says, and where the industry still has open problems to solve. TL;DR Summary IDP is moving beyond basic extraction. By 2027 it will function as a knowledge driven and multimodal system that understands documents, retrieves context, answers questions, and triggers business actions. Agentic AI takes IDP from extraction to action. Instead of only extracting fields, AI agents can validate data, flag discrepancies, request missing information, and route exceptions through multi step workflows. Retrieval Augmented Generation (RAG) is becoming the core knowledge layer for IDP, grounding AI answers in enterprise documents and improving traceability. Multimodal AI is improving document understanding by processing text, tables, handwriting, signatures, images, and complex layouts together rather than as plain text. Hyperautomation connects IDP with RPA, process mining, ERP, and CRM systems to automate document driven processes end to end. Low code and no code platforms are making IDP accessible to business users who are not trained programmers or data scientists. Domain specific small language models (SLMs) offer a cost efficient, private alternative to large general purpose models for document processing. Governance and responsible AI are becoming essential as IDP systems take on more autonomous decision making. Conversational IDP lets users ask questions in natural language and receive answers grounded in enterprise documents. GraphRAG improves reasoning across complex documents by connecting entities, clauses, dates, and regulations in a knowledge graph. Privacy preserving and sustainable IDP, including edge processing and efficient models, is gaining importance as data residency rules tighten. The core shift by 2027 is from digitizing documents to building governed, intelligent workflows that balance automation, accuracy, security, and human oversight. Trend 1: Agentic AI Moves IDP From Manual Review to Automated Action The most significant shift in IDP is the rise of agentic AI. Traditional IDP extracts data, agentic IDP reasons, plans, and executes multi-step workflows. It can read an invoice, compare it against ERP records, flag a mismatch, request the missing purchase order number, and forward the exception to the right team, all without a human clicking through each step. Gartner forecasts that agentic AI will be embedded in 33% of enterprise software applications by 2028, up from less than 1% in 2024. According to UiPath’s State of the Agentic Automation Professional report, a majority of automation professionals say they are already using or experimenting with agentic automation in production or pilot environments. Gartner also cautions that more than 40% of agentic AI projects could be canceled by the end of 2027 due to unclear value, rising costs, and weak risk controls. Many vendors are relabelling existing RPA or chatbot products as agentic without real autonomous capability, a practice Gartner calls agent washing. For document heavy industries such as banking, insurance, healthcare, legal, logistics, and government, the opportunity is real, but success depends on confidence scoring, human in the loop escalation, audit trails, and continuous supervision rather than autonomy for its own sake. Trend 2: RAG Becomes the Knowledge Layer for Document Intelligence Retrieval Augmented Generation (RAG) is becoming the backbone of modern IDP. RAG combines information retrieval with generative large language models. Instead of relying only on what a model learned during training, RAG retrieves relevant content from enterprise knowledge bases, vector databases, or document repositories and uses that content to ground the model’s response. In IDP, RAG shifts the focus from extraction to comprehension. A user can ask, “What is the termination clause in this contract?” or “Which invoices are overdue by more than sixty days?” The system retrieves the most relevant document sections and generates a cited, context aware answer instead of a raw data dump. RAG delivers several concrete benefits for document workflows: Reduced hallucinations, because answers are grounded in the source documents rather than model memory alone. Traceability, since citations back to the original passage support audit and compliance review. Conversational access, letting users query documents in natural language instead of searching folder by folder. Better exception handling, as agents can retrieve relevant policies, past cases, and business rules before acting. Scalable knowledge access, connecting IDP to contracts, claims, emails, and reports without retraining the underlying model. By 2027, three RAG variants are likely to dominate IDP deployments. Agentic RAG lets AI agents decide when to retrieve information and how to act on it. GraphRAG combines knowledge graphs with vector search to support multi hop reasoning across related documents. Multimodal RAG retrieves and reasons over text, tables, images, stamps, signatures,

Artificial Intelligence

Best Free AI Models for Document Processing in 2026: Ranked

Shubhankar Biswas / September 22, 2026

Back to Blog Table of contents On this page Best Free AI Models for Document Processing in 2026: Ranked Home › Blog › Artificial Intelligence › Best Free AI Models for Document Processing in 2026: Ranked Categories AI Tags Data Extraction OCR Algodocs By Shubhankar Biswas Published September 21, 2026, 10:15 The AI revolution has changed lots of things for us today, and data extraction is one of them. Previously, processing data from PDFs, images, and handwritten notes was difficult due to technological challenges. You needed to learn enterprise-level technologies to extract data from even a simple PDF document. But with the arrival of AI, things have changed drastically. AI models have seen significant improvements in their document processing accuracy and OCR capabilities. Recent research has also explored how modern vision-language models can improve document understanding and information extraction from complex documents. Research on AI-based document processing highlights the rapid development of these capabilities and the growing role of AI in understanding and extracting information from documents. Many platforms now publish AI and OCR leaderboards featuring their own accuracy benchmarks to track which models are leading the document processing and OCR race. What previously used to be a long and complex task can now be done by simply uploading a file and writing a few lines of a prompt. This might be easy for some people. But when it comes to processing a large amount of complex layout documents, where does AI stand? That is something we need to understand. In this blog, we will discuss the best free AI models for document processing that anyone can use in 2026. We will also look at the pros and cons of commercially available AI models for document processing and how to use these AI models for document processing. But first, we need to understand what AI document processing is. TL;DR AI has made document processing much easier than it used to be. Today, you can upload PDFs, images, handwritten notes, and other documents to AI models and extract data using a simple prompt. We tested several commercially available AI models using a handwritten invoice to see how well they could extract data from documents. The main findings are: ChatGPT: Easy to use and offers good data extraction accuracy. Gemini: Provides good extraction accuracy but may struggle with complex layouts. Claude: Can extract data accurately and keep it in a structured format, but may still miss some information. Grok: Can process PDFs, images, and handwritten notes but can struggle with complex layouts. DeepSeek: Offers good accuracy for PDFs, images, and handwritten notes and is free to use. Meta AI: Easy to use and free, but its extraction accuracy is less consistent. Mistral: Performs well with complex document layouts and has its own OCR capabilities. For personal use, free AI models can be useful when you only need to process one or two documents. However, processing a large volume of documents requires better control, customization, and accuracy. What is AI Document Processing? AI document processing means using AI/ML-based tools to process unstructured data from PDFs, images, handwritten notes, and other document types and convert it into structured data. These AI models use machine learning and deep learning algorithms to understand the context of a document, capture the required information, and extract the data. Users can use commercially available AI platforms such as ChatGPT, Gemini, or Claude to upload a document, write a prompt, and extract data from it. But there are other document processing tools such as Algodocs, Nanonets, and Docsumo that also offer AI capabilities similar to commercial AI models for document processing. However, these AI data extraction tools go beyond basic data processing and can be used for complex data extraction and analysis tasks. Best Free AI Models for Document Processing in 2026 The current market is inundated with AI platforms that you can use to process data from various types of documents. You have commercial AI/ML tools, and then there are open-source data processing tools as well. Some tools run on browsers or desktops, while some can be run locally on your own servers. We have tried 6 commercially available AI models to extract data from documents. We used the free version of each tool with simple prompt instructions such as “extract data from this document” to see how accurately these tools can process data from a document. For this experiment, we used a handwritten invoice to see how well each AI model could process and extract the data. So, here are the best free AI models you can use for document processing in 2026. ChatGPT by OpenAI We all know what ChatGPT is and what it can do. But ChatGPT is also a great AI model for document processing. It is easy to use, and its accuracy is great. To extract data from a document with ChatGPT, all you need to do is upload the file and write your prompt. Once you hit enter, ChatGPT will start extracting the data. You can then copy the extracted data into a file such as a .doc or .excel file and save it. Pros. The biggest pro of ChatGPT is that it is easy to use and the data extraction accuracy is very good. You can tell it which field you want to extract, and it will capture and extract the required information. Cons. You need a monthly Pro plan subscription to use better models. Price: $20 USD Per month. Free Plan: Yes Available Gemini Google’s flagship AI model is a great AI tool for data extraction. One of the reasons to try Gemini for document processing is because Google has developed its own OCR model, which uses AI to extract data from documents. Gemini uses Google’s OCR engine for data extraction alongside AI. You can extract data from a document simply by uploading the file and writing your prompt. Once you hit enter, it will start extracting the data. You can copy the data into a file such as a

Data Parser : Everything You Need To Know

Data Extraction

Data Parser Explained: How It Works & Why Your Business Needs One

Shubhankar Biswas / September 17, 2026

Back to Blog Table of contents On this page Data Parser Explained: How It Works & Why Your Business Needs One Home › Blog › Data Extraction › Data Parser Explained: How It Works & Why Your Business Needs One Categories Data Extraction Tags AI Data Extraction OCR Algodocs By Shubhankar Biswas Published September 17, 2026, 09:35 Most companies don’t have a data problem. They have a document problem. Invoices, purchase orders, bills of lading, and warehouse receipts still arrive as PDFs, scanned images, and occasionally handwritten paper. None of that is directly usable by an ERP, a WMS, or a BI dashboard. Someone has to read each document and retype the numbers, and every time a human retypes a number, there’s a real chance it comes out wrong. Published benchmarks put manual data entry error rates at roughly 1% to 5% of fields under normal conditions, and seminal by Professor Raymond Panko on human cognitive error in spreadsheets and documents has demonstrated that cell and field error probabilities routinely climb to 18–40% under time pressure, lack of validation, or with messy source material. A data parser is the software that removes that step. It reads a raw document, identifies the fields that matter, and outputs clean, structured data your systems can use immediately, with no retyping required. This guide explains what a data parser actually does, how it’s different from OCR and data extraction, the main parsing methods in use today, and how the technology plays out in a specific high-friction workflow: warehouse receipt data extraction. Real benchmarks and sources are cited throughout so you can verify the numbers yourself. TL;DR Summary Core Function: A data parser converts unstructured or semi-structured content (PDFs, scans, emails, web pages) into structured formats like JSON, CSV, or XML. Separation of Concerns: Parsing is distinct from OCR (which makes text machine-readable) and extraction (which pulls specific values). Most modern platforms combine all three. Human Error Risk: Manual data entry error rates typically run 1–5% of fields and can spike to 18–40% on complex documents, according to Panko’s human error research. Accuracy Leap: Modern AI-based parsers report 99–99.9% accuracy on structured fields in production, compared with roughly 80–85% real-world accuracy for legacy rule-based OCR. Cost & Time Savings: Automating extraction cuts per-document processing cost from an estimated $5–$25 down to roughly $2.88–$4 (a 60–80% reduction) and cuts processing time by 60–70%. Market Expansion: The intelligent document processing (IDP) market is growing fast: estimates benchmark the sector at ~$2.30 billion in 2024 according to Grand View Research and Precedence Research, climbing to over $10–$13 billion per Fortune Business Insights, with forecasts projecting a 25–34% CAGR through the early 2030s. High-ROI Use Case: Warehouse receipt data extraction removes manual re-keying from inventory reconciliation and prevents downstream shipping and ledger discrepancies. What Is a Data Parser? A data parser is a software or data parsing tool that extracts raw, unstructured data from websites, documents, multimedia, databases, and other sources. There is a difference between data extraction and data parsing. People often get confused between a data parser and a data extraction tool, but both are completely different. A data parser is specifically designed to extract data from various sources, particularly webpages, databases, and other sources such as website code, PDFs, images, videos, libraries, and other data sources. However, it does not convert this unstructured data into a structured data format. For example, a data parser can be used to extract data from an ecommerce website. Users can define the fields and rows, such as product prices, product descriptions, metadata, and other details, from the website and save the extracted data for later use in their work tasks. How Does a Data Parser Work? Most data parsers (rule-based, machine-learning-based, or hybrid) follow a similar four-stage pipeline: Step 1: Input and Document Classification The parser ingests a file: a PDF, scanned image, email, or web page. It typically classifies the document type first (invoice, bill of lading, warehouse receipt) because different document types need different extraction logic. Modern systems use trained classifiers for this step rather than relying on the user to tag every file manually. Step 2: Preprocessing Raw input is rarely clean. Scanned documents may be skewed, low-resolution, or faded; HTML pages are cluttered with navigation and ads. For scanned or photographed documents, optical character recognition (OCR) converts the image into machine-readable text at this stage. Preprocessing quality has an outsized effect on final accuracy: a blurry or skewed scan degrades every step that follows. Step 3: Parsing and Field Extraction This is where the actual interpretation happens, using one of three approaches: rule-based, machine-learning-based, or hybrid. The output of this step is a set of identified fields (supplier name, invoice number, line-item quantities) mapped to their values. Step 4: Structuring and Output The extracted fields are formatted into a structured output (JSON, CSV, XML, or a direct database write) and delivered to the target system, whether that’s an ERP, WMS, CRM, or analytics platform. A process that would take a person several minutes per document typically completes in seconds. Data Parsing vs. Data Extraction vs. OCR These three terms get used interchangeably, but they describe different jobs in the same pipeline. Term What It Does Analogy OCR Converts an image of text into machine-readable characters Turning a photograph of a page into typed text Data Extraction Locates and pulls out specific values from that text (a date, a total, a name) Underlining the important numbers on the page Data Parsing Interprets the structure of extracted content and organizes it into usable fields Filing those underlined numbers into the correct labeled boxes on a form In short: OCR makes text readable, extraction finds the data, and parsing gives that data structure. A complete intelligent document processing (IDP) system uses all three together, and most commercial platforms now bundle them into a single workflow. Types of Data Parsing Not every document needs the same parsing approach. The right method depends on how consistent your documents are. Rule-Based

supply chain

Warehouse Receipt Data Extraction: How AI Automates Warehouse Document Processing

Shubhankar Biswas / September 11, 2026

Back to Blog Table of contents On this page Warehouse Receipt Data Extraction: How AI Automates Warehouse Document Processing Home › Blog › Supply Chain › Warehouse receipt data extraction using AI Categories Supply Chain Tags Logistics Receipt Data Extraction By Shubhankar Biswas Published September 11, 2026, 11:35 Industries such as warehouse, logistics, transportation, and supply chains deal with various types of documents on a day-to-day basis. The global warehouse market is expected to reach $3.3 trillion USD by 2030 according to a report. These data can be in the form of PDF, scanned images, paper documents, images, or other digital file formats. The layout and data structure of these documents can be different depending upon the organization and industry. The supply chain and logistics industry often use documents such as warehouse receipts to keep track of the goods record that is stored in storage facilities. These documents can contain information such as product quantities, specifications, serial numbers, etc. in an unstructured format. Extracting data from warehouse receipt documents is a challenging task. That’s why various enterprise tools are designed to automate warehouse receipt data extraction effectively and smoothly. In this article, we will discuss what warehouse receipt data extraction is all about, challenges with warehouse receipt data extraction, technologies involved, the use of AI in warehouse receipt data extraction, and how Algodocs can effectively automate data extraction from warehouse receipts and other logistics and supply chain documents. TL;DR Warehouse receipt data extraction uses AI and automated technologies to convert data from paper, scanned, PDF, image, and digital warehouse receipts into structured, usable data. Warehouse receipts may contain important details like receipt numbers, warehouse information, depositor details, commodity details, quantities, grades, lot information, storage charges, and insurance information. A typical warehouse receipt data extraction pipeline includes document ingestion, image pre-processing, OCR, AI-based document understanding, field extraction and table extraction, validation, and structured data export. OCR turns text from scanned documents into machine-readable data, while AI, ML, NLP, and intelligent document processing (IDP) help understand document context, identify fields, and extract information from tables and complex layouts. Automated extraction of data from warehouse receipts can reduce manual data entry, reduce human error, improve processing speed and data consistency, and enable businesses to process higher volumes of documents. Businesses should consider features such as extraction accuracy, document layout handling, table extraction, integrations, scalability, security, and cost when selecting an extraction tool. Algodocs leverages AI, ML, OCR & IDP for field, table, row, and other information extraction from warehouse receipts and supply chain documents, offering custom extractors, validation, API automation, and exports to Excel, CSV, JSON, and XML. What Is Warehouse Receipt Data Extraction? The term warehouse receipt data extraction means using automated technologies and AI to automate capturing and extracting unstructured information from a warehouse receipt and converting it into structured digital data. A warehouse receipt is generally issued by a warehouse operator that tells that goods have been received and are being held for a depositor or holder. A warehouse receipt can contain information such as size, weight, dimension, receipt number, consignee details, and other information. So What Is a Warehouse Receipt Document? A warehouse receipt is a type of confirmation document issued by a warehouse company to a goods company when it receives goods from the goods company. This document serves as a legal proof that the warehouse has received a certain amount of goods that will be stored in the storage facility. A normal warehouse receipt document may contain the following information: Warehouse receipt number Warehouse registration number Warehouse name and address Depositor name and address Issue date Commodity name Quantity and unit of measurement Commodity grade or quality Lot or package information Storage and handling charges Market value Insurance information The information can vary depending upon the format and local regulations. But these data are the standard data you will find in a warehouse receipt. How Warehouse Receipt Data Extraction Works While the older method involves capturing and extracting data by humans and updating those data in a system, modern warehouse receipt data extraction typically follows a series of steps: Automate warehouse receipt documents and other supply chain documents using Algodocs pre-built custom AI models. Extract data with 99% accuracy and 10X speed. Sign Up Today 1. Document Ingestion In this process warehouse receipts enter the system. These documents can be in formats such as: Scanned paper receipts PDF files JPG or PNG images Email attachments Documents stored in cloud storage Digitized electronic receipts An automated document processing platform can collect these files from different sources and send them into the extraction workflow. 2. Image Preprocessing In this process the uploaded documents are scanned and analyzed, then turned into a more visible doc or image. In this process layout improvement, text enhancement, text resizing, and other steps are done so that the core system can capture data more accurately. Basically, this step helps OCR and AI models interpret the document more reliably. 3. OCR and Text Recognition One of the most important steps where Optical Character Recognition, commonly called OCR, converts text from an image or scanned warehouse invoice document into machine-readable text. 4. Document Understanding (AI & ML, NLP) After text recognition is done through OCR, the AI and ML models analyze the document’s layout and context. This ensures that data can be segregated, contextually analyzed, and filtered. 5. Field and Table Extraction A warehouse receipt can contain both individual fields and tables. The information could be the receipt number, date, product dimension, etc. With the help of IDP and AI, data from tables, rows, and fields are extracted systematically. A modern warehouse receipt data extraction solution should be capable of handling both key-value fields and tabular information. Note: Algodocs supports key-value extraction and table extraction, including table data spanning multiple pages. 6. Structured Data Export The final step is turning extracted information into a format that other applications can use. Depending on the workflow, the data may be exported as: Excel CSV JSON XML API

Healthcare

How to Automate Patient Chart Data Extraction and Streamline Healthcare Data Processing Using AI and IDP

Shubhankar Biswas / September 1, 2026

Back to Blog Table of contents On this page How to Automate Patient Chart Data Extraction and Streamline Healthcare Data Processing Using AI and IDP Home › Blog › Healthcare › How to Automate Patient Chart Data Extraction Categories Healthcare Tags AI Data Extraction Healthcare Healthcare OCR By Shubhankar Biswas Published September 1, 2026, 09:35 Healthcare organizations deal with huge amounts of patient information every day. The majority of this information is stored in physical documents, PDFs, scanned images, or handwritten notes. These documents include intake forms, medical reports, lab reports, prescriptions, insurance documents, clinical notes, scanned records, and, most importantly, patient charts. A recent study shows that 80% of healthcare data is stored in an unstructured format, and processing data from these documents using legacy EHR platforms (Electronic Health Record) can be challenging. A patient record can be a manually filled document or may arrive in PDF, scanned image, fax, photograph, or Word document format. Staff members often have to manually read these documents, find important information, type it into another system, and check it for errors. This process takes time and can create unnecessary work for healthcare teams. But modern AI-based data extraction tools, such as intelligent document processing, Optical Character Recognition (OCR), Large Language Models (LLMs), and Artificial Intelligence can streamline healthcare and patient chart data extraction processing. This article explains how patient chart data extraction works, why traditional methods create problems, how AI and IDP improve the process, and how organizations can build a reliable healthcare document processing workflow. TL;DR AI-powered patient chart data extraction can help healthcare organizations: Extract patient and contact information Capture medical history, medications, and allergies Identify diagnosis and procedure information Extract insurance details Process lab reports and test results Handle scanned documents, images, and handwritten forms Reduce manual data entry Improve data consistency Connect extracted information with EHRs and other systems Process large document volumes faster A modern healthcare extraction workflow typically combines OCR, document classification, AI, LLMs, validation, human review, and system integration. The goal is not simply to read healthcare documents. It is to convert them into reliable structured data that can support real healthcare workflows. What Is a Patient Care Document? A patient care document is a healthcare record that is used to capture information about a patient’s identity, medical history, treatment, diagnosis, and other details during the course of care. Common examples include: Patient intake and medical history forms Clinical notes and discharge summaries Lab and diagnostic reports Prescription and referral forms Insurance documents and medical bills These documents may be printed, scanned, digitally created, or contain selectable text. They can also include tables, checkboxes, signatures, stamps, and handwritten notes. For example, a patient intake form may contain: Patient information: Name, date of birth, gender, address, phone number, and email. Medical information: Existing conditions, previous surgeries, allergies, medications, and family history. Insurance information: Provider, member ID, group number, and policy number. Patient care document data extraction uses automated technologies to identify this information and convert it into structured data for healthcare applications and workflows. What Is Patient Chart Data Extraction? Patient chart data extraction means the process of extracting data from patient care documents using automated data extraction technologies to improve healthcare workflow. This extracted information can include patient details, medical history, medications, allergies, diagnoses, lab results, insurance information, and other clinical or administrative data. Data extraction technologies such as IDP, OCR, AI, and ML can automate this process across PDFs, scanned documents, images, forms, and other healthcare records. Challenges With Patient Chart Data Extraction Healthcare documents are more complex than standard business forms. They can contain different layouts, scanned text, handwriting, tables, medical terminology, and sensitive patient information. Extracting data from a patient chart or any other health document can come with the following challenges: Multiple formats: A patient chart or any other health document can be a PDF, scan, image, fax, Word file, or handwritten form. Different layouts: These documents can have different layouts and structures, as every hospital has its own document layout. Poor scan quality: Patient charts can sometimes be blurry, skewed, have shadows, or have low resolution, which can affect data extraction accuracy. Medical terminology: Abbreviations, drug names, codes, measurements, and clinical terms require contextual understanding. Accuracy: Extracted information needs validation, with uncertain records sent for human review. Data security: Patient information must be processed and stored using appropriate security and privacy controls. Manual vs. Automated Patient Chart Data Extraction Pros & Cons Manual data extraction requires hospital staff to read healthcare documents, identify relevant fields, and enter the information into another application. This method is only reliable if the number of documents is low, but as the workload increases, the document volume grows. Extracting data from 1,000 or 10,000 patient charts becomes difficult manually. But an AI-powered HIPAA-compliant data extraction tool can automate this process instantly. Here is the difference between manual and automated patient chart data extraction: Factor Manual Extraction AI and IDP Extraction Processing speed Slow Fast Data entry Human-driven Automated Scalability Limited High Format handling Depends on staff Supports varied layouts Validation Manual Automated plus human review Data output Manually entered Structured automatically Integration Often manual API and workflow-based Staff effort High Lower With automation and AI, the automated data extraction method also uses a human-in-the-loop approach so that data accuracy and authenticity can be improved during the extraction process. A strong workflow combines automated extraction with validation and human review for uncertain or high-risk records. How to Extract Data from Patient Chart Documents Now, the patient chart data extraction workflow can be multi-step, depending on the tools or method an organization chooses. But this is a recommended and reliable patient chart data extraction workflow a healthcare organization should use. 1. Collect Patient Documents A hospital or any other healthcare organization collects various types of health records. These documents may come from scanners, email attachments, EHR exports, shared folders, fax systems, cloud storage, or other sources. These files should first be brought into a controlled processing environment.

Data Extraction

Best Software for Automating Document Processing: A Practical Guide for 2026

Shubhankar Biswas / August 24, 2026

Back to Blog Table of contents On this page Best Software for Automating Document Processing: A Practical Guide for 2026 Home › Blog › Data Extraction › Best Software for Automating Document Processing Categories Data Extraction Tags algodocs automation data extraction By Shubhankar Biswas Published August 24, 2026, 09:35 Modern business runs on data. The more data a business can capture, process, and analyze, the better the outcome. But to process data in large quantities, you need a reliable tool. That technology is known as document processing. However, choosing the best software for automating document processing is often difficult. Documents come in various forms, including invoices, bills, financial reports, contracts, and even handwritten notes. To process all of these efficiently, you need robust IDP tools that can capture, extract, and automate the entire journey in just one click. Finding a reliable solution among the thousands of document processing applications available on the market can be overwhelming and frustrating. Every vendor claims to have the most accurate AI, the fastest engine, or the easiest interface. But the reality is that the right choice depends entirely on your specific workflow, document types, and business goals. That is why this guide exists. It will help you cut through the noise and find the best tool for document processing for your specific needs. But before we compare platforms, let us first understand what document processing actually is and why it matters so much in today’s data-driven world. . TL;DR Summary Choosing the best software for automating document processing does not have to be complicated. Automated tools save time, reduce errors, and scale with your business. Cloud document capture has become the standard for modern teams. Algodocs stands out as a top choice because it offers template-free AI extraction, strong security, and affordable pricing. Start with one document type, test with real documents, and keep a human in the loop for exceptions. What is Document Processing? Document processing is the method of capturing and extracting data from various types of documents, such as invoices, bills, passports, and contracts, using automated technology. These tools can be computer software like OCR (Optical Character Recognition), IDP (Intelligent Document Processing), ICR (Intelligent Character Recognition), or AI (Artificial Intelligence). The oldest method is manual document processing, where humans identify and extract data by hand. This approach is slow, expensive, and full of errors. Anyone who has ever spent hours typing data from PDFs into an Excel spreadsheet knows exactly how painful this process can be. On the other hand, automated tools use technology to complete the process with greater accuracy and faster speed. We often call this cloud document capture, as the data is extracted using web-based tools and saved on a secure cloud platform. This is the foundation of the best AI document processing available today, as it removes the physical limitations of local hardware and allows teams to work from anywhere. Instead of being tied to a desk with a scanner, employees can simply upload a photo from their phone or forward an email to a processing inbox. The system handles the rest. The shift from manual to automated processing is not just a convenience; it is a competitive advantage. Businesses that automate their document workflows can process more volume with fewer resources, reduce costly errors, and free up their human talent for higher-value work. This is why so many organizations are actively searching for the best software for automating document processing to stay ahead of the curve. How Does Document Processing Work? In the manual method, a human expert uses analytical skills to read data from a document and then types that data into another file format. This is not only time-consuming but also mentally draining. After hours of repetitive data entry, even the most careful employee will start making mistakes. Those mistakes can lead to incorrect invoices, missed deadlines, and unhappy customers. In the automated method, computer software handles everything from start to finish. The system first captures the document, whether it arrives as a PDF, a scanned image, or a photo taken on a smartphone. Next, it extracts the relevant data points using AI algorithms that can identify names, dates, amounts, addresses, and other key fields. After extraction, the system validates the data to ensure it meets certain quality thresholds. If the confidence score is low, the document is routed to a human reviewer for a quick check. Finally, the validated data is exported to a database, CRM, or ERP system for further use. Once extracted, the data can be saved locally or on a cloud drive. From there, it can be shared with team members or integrated with third-party business apps to support smarter business decisions and organizational growth. This automated flow is the core advantage that separates modern IDP tools from outdated manual processes. The best part is that this entire journey can happen in seconds, not hours. A document that would take a human five minutes to process manually can be handled by software in under ten seconds. When you multiply that across thousands of documents per month, the time savings become enormous. Manual vs. Automated Document Processing There is a major difference between manual and automated data extraction methods. Understanding this difference is essential before you invest in any software. The table below highlights the key distinctions:y. These are the major differences. Features & Difference Automated Data Extraction Manual Data Extraction Speed Fast Slow Accuracy Accurate Not Accurate Cost Affordable Expensive Automation Fully Automated Not Available 3rd Party Integration Possible Not Possible Scalability Highly Scalable Not Scalable Best Suited For All types of business Limited to small business Human Supervision Only during exceptions Very High The biggest reason automated data extraction beats manual is reliability. Manual processing loses its value when dealing with large volumes. You cannot process 10,000 documents in a day with humans alone. Even if you hired a team of twenty data entry clerks, they would still struggle to keep up with that kind of volume, and

PDF

How to Edit PDF In Word: A Step By Step Guide

Shubhankar Biswas / August 17, 2026

Back to Blog Table of contents On this page How to Edit PDF In Word: A Step By Step Guide Home › Blog › PDF › How to Edit PDF In Word: A Step By Step Guide Categories PDF Tags algodocs how to guide pdf data extraction By Shubhankar Biswas Published August 17, 2026, 09:35 PDF is a widely used file format that helps to store, send, and share data with others. Editing content in a Microsoft Word file is a straightforward and easy process. But the question arises: how do we edit PDF in Word if we have to use Word as a pdf editor? The answer is surprising yet amusing. I can bet lots of people who work with various types of documents don’t even know that it is possible. Since you can edit a PDF file using Adobe Acrobat or any other office PDF editor, editing a PDF using the Word app takes a different approach. But before we learn how to modify a PDF file in Word, we need to understand why we should, or why we have to, edit a PDF in Word. But first, let’s understand what a PDF and a Word application are. TL;DR Summary Microsoft word can be used as a PDF editor tool but it has lots of limitations when it comes to editing a pdf file. A native pdf editor offers complete pdf editing experience and you can also edit scanned image pdf and other pdf files. To edit pdf in word, just open Word app go to File > Open > Browse navigate the PDF file in your computer. Edit the pdf file and Save edited file as a PDF file. What is a PDF file A PDF is a document file type; it uses the .pdf file extension, and it was developed by Adobe in 1993. The full form of PDF is Portable Document Format, and it is widely used for creating and sharing text or image data in a PDF file and sharing it as an email attachment. A PDF file can be an invoice, receipts, scanned handwritten notes, an image file, etc. If you want to learn more about the PDF file or its technical specifications, you can check the pdf.org official page. What is Microsoft Word Word, also known as Microsoft Word, is a famous document editor used for editing .docx files. It’s a very popular text document editing app that can be used in various ways. One way to use Microsoft Word is to edit a PDF file. We will discuss that in this blog. If you want to learn more about the Word app, you can check the official product pageMicrosoft Word. Difference Between Editing a PDF File in PDF Editor VS Word App Editing a PDF file in a PDF editor vs editing a PDF file in a Word app is a very different process. As you know, PDF viewers and editors are built for editing a PDF file exclusively. Whereas a Word app acts as a PDF editor with limited PDF editing functionality. These are the major differences. Feature PDF Editor Word Editor Cost Completely Free Mostly Free ( But You Need Microsoft 365 Plan to use the app) Editing Accuracy 100% Precision. Keeps the original layout Does the basic Editing only Fillable forms Fully supported natively Not supported natively Redaction Not available Available Digital Signature Only via insert method Natively, e-signature and other methods available Scanned pdf edibility Not available Available Formatting accuracy Superiorly accurate Poor Supported PDF types Native, Scanned PDF & Other Layouts Only Native PDF Layouts How to edit a PDF in Word? ( Windows machine ) Please follow these instructions to edit a PDF in Word Step 1 Click on the Word App icon on your desktop, or click on the Start bar in Windows and search for “Word” then click on the icon. Now go to File > Open > Browse, and then navigate to your file.. Step 2 In our case, we have our PDF file in the Downloads section. So click on the PDF file. Click on the PDF file and then click the Open button in the bottom left-hand corner. Now your file will open, and a message will be shown in the app. Click on the OK red button. Step 3 Now click on the Enable Editing option. Step 4 Now you can edit any text and data in your PDF file. Step 5 After editing the file, click on Save and select the file type you want to save. Pick PDF and click on Save, and your edited file will be saved. Challenges With Editing A PDF In Word? While editing a PDF in the Word app might sound easy, it poses many challenges compared with editing a PDF file using a native PDF editor. Some major issues with editing pdf using a Word App are. Broken layout : When you are editing a PDF using the Word app, there is a chance that the overall PDF document layout can be broken due to Word. Microsoft Word app often breaks the layout of the actual PDF document so that you might see an inconsistent, broken layout, changed fonts, mismatched text alignment, and other issues. Missing Data: Due to its nature, the Word app can mess with your raw data before editing and after saving the edited file. When you open a PDF file in the Word app, it asks to convert a PDF file to Word format. Since it doesn’t directly add or remove data in a PDF file, it converts the PDF file to a Word file and then adds the data. This file conversion process corrupts the raw data, and you end up with missing data. Limited PDF file type support: This is one of the main factors why businesses avoid using Word as a PDF editing tool. A native PDF editor can edit various types of PDF files, including native PDFs, scanned PDFs, fillable PDF forms,

Image Data Extraction

How to Extract Text from Image: Tools, Methods, and Best Practices

Shubhankar Biswas / August 7, 2026

Back to Blog Table of contents On this page How to Extract Text from Image: Tools, Methods, and Best Practices You Must Know Home › Blog › Image Data Extraction › How to Extract Text from Image: Tools, Methods, and Best Practices Categories Image Data Extraction Tags AI Data Extraction how to guide image data extraction image text extraction By Shubhankar Biswas Published August 7, 2026, 06:38 Updated August 7, 2026, 06:40 Introduction Text data can be carried through various types of document file extensions: a PDF, Word, or CSV, as well as an image. But when you need to extract text from image files, the process can get complicated fast, largely because of file complexity and how the text sits within the picture. That’s why so many people search for a reliable image text extractor: a tool that lets you pull text from image content quickly, whether you prefer an online text from image extractor or an offline desktop app. An image with text data is used in various business and personal settings. A business may receive a scanned invoice in image format that contains important financial data. A large business can also record keep old files by scanning paper documents as image PDF files to save physical space in an office. In this blog, we will discuss how to extract text from image files, the challenges involved, the best tools for the job, and other useful tips. But before we start, what exactly is image text data? TL;DR Summary Extracting text from image files means pulling readable text out of a picture, screenshot, or scanned document so it can be edited, searched, or stored digitally. You can do this manually or, more effectively, with an automated text extractor powered by OCR (Optical Character Recognition) or IDP (Intelligent Document Processing). Automated tools like Algodocs, Nanonets, Rossum, and Klippa let you extract data from image files with higher accuracy and speed than manual entry, especially for blurry, skewed, or complex documents. When choosing a picture text extractor, consider features, pricing, data privacy, and integrations. What Is an Image with Text Data An image with text data can be a screenshot or scanned copy of a document created as a .jpg or .png file format. You will also find scanned image text files in PDF format, which is very common in business settings. You may have a website opened on your cellphone and want to save some visible part of text or data, so you take a screenshot and it becomes a text image file. Similarly, you might take multiple screenshots of a website, or a preview of a file in your browser that you can’t save, but you still want that text data stored somewhere, so you take screenshots and turn those screenshots into a PDF, or simply keep them as they are to save the data on your computer. Where the Image with Text Data Is Used As discussed earlier, an image text file is used in various ways, from personal data keeping and sharing to sending data files as email attachments. Many organizations safekeep old records as scanned image text files. A vendor can send a handwritten invoice to a supplier via a scanned image email attachment. So the uses of image text data can be seen in many scenarios, which is why the ability to extract text from image files becomes more important. Challenges with Image Text Data One of the biggest challenges with image text extraction is the quality of the image and how visible the text and data are within the file. As you know, scanned image quality can be poor. Oftentimes, document quality and text visibility are hurdles for users or machines trying to capture the data from an image. Old, blurry documents are the most troublesome cases, where blurry, skewed fonts, broken page layouts, and torn images create the most trouble. Trying to pull text from image files like these often becomes nearly impossible. Even OCR based data extraction technologies struggle to understand, capture, and extract data from these types of image text documents. Manual vs Automated Image Data Extraction There are primarily two ways to extract data from an image: manually or through an automated method using OCR and IDP. Manual data extraction is prone to many errors, and its data accuracy is not reliable. Automated methods using technologies such as OCR, IDP, AI, and NLP offer higher data extraction accuracy, faster extraction speed, better control, and third-party automation, which makes them the preferred choice for anyone who needs to extract data from image files regularly. How Do You Pull Text from Image Files (Step by Step) As discussed above, there are two primary methods of extracting text from an image. But using automated methods, such as AI based data extraction tools and OCR tools, gives you far better results than typing everything out by hand. We have previously covered this topic in another blog dedicated entirely to how to extract text from image files. You can read our blog on this for a detailed walkthrough. Technologies Behind Image Text Extraction One of the foundations of image data extraction is OCR technology, which has been around for many years and remains a common way to turn image into text. OCR is widely used for data extraction from many types of files and formats. Then came ICR, which was also useful, with a few updated features. But in recent times, a new technology emerged known as IDP (Intelligent Document Processing). IDP has taken the ability to extract text from picture files to a whole new level. IDP can extract text data from very poor, skewed, and blurry images, which was not possible with OCR and ICR alone. Since IDP uses machine learning and artificial intelligence to capture and extract data, this technology is highly effective for precision and faster text extraction. Best Tools for Extracting Text from Image There are many tools you will find on the internet to pull text from

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Author name: Shubhankar Biswas