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data extraction from images

bill of lading

How Algodocs enhances bill of lading processing

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Back to Blog Table of contents On this page How Algodocs enhances bill of lading processing Home › Blog › bill of lading › How Algodocs enhances bill of lading processing Categories bill of lading Tags ai platform for data extraction algodocs Bill of Lading data extraction from images ocr api web-based ai platform for data extraction By Shubhankar Biswas Published October 13, 2025, 12:24 Updated July 23, 2026, 07:35 A bill of lading, shortened as BL or BoL, is a legal document given by a carrier (a company that provides transportation) to the shipper. It outlines the particulars of the goods being transported, including the kind, quantity, and destination of the goods. In addition, a bill of lading functions as a shipping receipt when the carrier finalizes the delivery of the goods after a given destination. This document must accompany the shipped products, no matter the form of transportation, and must be signed by an authorized representative from the carrier, shipper, and receiver. What Is the Purpose of a Bill of Lading? A bill of lading has three primary purposes. First, it is a document of title to the goods described in the bill of lading. Second, it is a receipt for the shipped products. Finally, it represents the agreed terms and conditions for the transportation and eventual release of the shipped goods. What Is in A Bill of Lading? Typically, a bill of lading will include the names and addresses of the shipper (consignor) and the receiver (consignee), shipment date, quantity, exact weight, value, and freight classification. Also included is a complete description of the items, including whether they are classified as hazardous, the type of packaging used, any specific instructions for the carrier, and any special-order tracking numbers. Why Is a Bill of Lading Important? A bill of lading is a legally binding document. The carrier and the shipper are given all the essential details to help them process a shipment correctly. Hence, it can be used in litigation if the situation requires it. The parties to it will be highly knowledgeable about the document as required by the law to ensure that there is no compromise in the safety and security of your goods. A bill of lading is undisputed proof of shipment. Furthermore, it allows for segregating duties, a vital part of a firm’s internal control structure, to prevent theft. Different Types of Bills of Lading Some of the most common include: Inland bill of lading Ocean bill of lading Through bill of lading Negotiable bill of lading Uniform bill of lading Challenges With Manual Processing Some of the known challenges are: Logistics companies are confronted with vast volumes of data through files in document form, such as invoices, airline bills, price lists, HR forms and payrolls, customs forms, and so on. Manual data management is time-consuming and error-prone. Human data entry errors can lead to costly consequences. Implementing Intelligent Document Processing (IDP) Much of the information needed to execute logistics and supply chain operations is manually extracted from data sources such as the Bill of Lading. Automating the processing of instructions for the Bill of Lading proves to be crucial in increasing back-office productivity and, consequently, improving customer service performance. If traditionally conducting these activities by manually copying and pasting data carries the risk of errors and is an obstacle to maximizing operational efficiency, the value of automation must be highlighted. Benefits Of Intelligent Document Processing (IDP) Accuracy: AI-driven IDP has the advantage of minimizing human factors and error occurrence, which in return produces more quality. Continuous Learning: AI models can learn from humans, thus enabling them to get better results even without human assistance. Cost Savings: Laboratory test costs decrease when data is not appropriately edited, prevented by IDP. Data Extraction: IDP excels in extracting names, dates, addresses, and amounts from BoLs. Quality Assurance: Human interaction ensures precision modeling. Guaranteed Quality: The IDP introduced AI computing and on-demand data collection to achieve valid results. CASE STUDY Imagine XYZ company, which is a logistics firm. It receives the shipment. The manager determines the type and amount of goods that need to be ordered. They then fill out a purchase order (PO), and XYZ’s owner reviews and initials each PO before it is emailed to the vendor. The vendor gathers the orders and signs a bill of lading along with a representative from the overnight carrier. The forwarder then supplies products to the ship and provides the invoice to the manager, who compares the bill of lading details with what was mentioned on the PO. If the information matches, the PO and the bill of lading are sent to the owner, who reviews the documents and writes a check payable to the vendor. Fields That Can Be Extracted: OT PRO Number Consignee City Consignee State Consignee Zip Total Weight Handling Unit Description The list goes on and on. Example of extract data output: Enhancing Accuracy with Automated Data Extraction Algodocs can extract data automatically from the Bills of Lading. This step dramatically improves efficiency and offers new prospects for success in logistics. Applying Algodocs to the Bill of Lading allows the necessary data to be automatically extracted from the PDFs and images of the handwritten document received from the carrier and a new document structure from the Bill of Lading to be created quickly, eliminating the need for repetitive and error-prone manual work. Improving Efficiency with Algodocs AI Algorithms Bill of Lading instructions are often accompanied by various documents in different formats, as each company chooses the format best suited to its needs when sending instructions to the carrier. Algodocs represents a breakthrough in managing Bill of Lading data extraction because Algodocs AI algorithms process text recognition; this process not only speeds up processing but also minimizes the possibility of errors. KEY TAKEAWAYS Algodocs is a potent system that combines OCR, NLP, and ML technologies to provide a tool capable of extracting data from heterogeneous documents. It is particularly useful in creating a Bill of Lading. With Algodocs, you can automatically extract any field

Algodocs, Data Extraction, Image Data Extraction

How to Extract Handwritten Data from PDFs with Algodocs?

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Back to Blog Table of contents On this page How to Extract Handwritten Data from PDFs with Algodocs? Home › Blog › Algodocs › How to Extract Handwritten Data from PDFs with Algodocs? Categories Algodocs Data Extraction Image Data Extraction Tags ai platform for data extraction algodocs character recognition data extraction from images handwritten pdf How to Extract Handwritten Data from PDFs web-based ai platform for data extraction By Shubhankar Biswas Published October 13, 2025, 12:19 Updated July 23, 2026, 07:36 Want to learn how to extract handwritten data from PDFs easily for your business documents? Algodocs is a powerful AI platform developed based on the latest technologies to streamline your processes and free your team from annoying and error-prone manual data entry by offering fast, secure, and accurate document data extraction. It helps you get rid of your workforce from repetitive, time-consuming, and error-prone manual data entry tasks such as extracting handwritten data. With its AI capabilities, Algodocs gives one of, if not the best, user experiences and interfaces. Areas and applications of Algodocs include extracting handwriting, tables, key-value pairs, marks, and signatures from PDFs and image files. Algodocs offers a forever free subscription, with 50 pages processed every month. What is OCR? Optical Character Recognition (OCR) engines are primarily focused on machine-printed text and may produce low accuracy for handwritten text. Intelligent Character Recognition (ICR) is an advanced recognition system that is used to recognize handwritten text. This allows the automatic conversion of text in an image into letter codes that are usable within computer and text-processing applications. Although many processes involve computer-based operations and are implemented in a digital environment, paper is still widely used across most core business processes such as mortgage origination, order fulfillment, contracts, and other documents that usually require handwritten input and signatures. Nowadays, the digitalization of paper documents plays an important role, and deciding on the right data capture software is critical since handwriting recognition, unlike printed text recognition is a more complex task that usually involves advanced deep learning algorithms. How Does Algodocs Do It? Handwritten data extraction from PDFs is implemented by converting handwritten text into machine-printed text with high accuracy. With the Intelligent Character Recognition (ICR) of Algodocs, you can automate your document processing workflow and get rid of manual data entry. Scan your paper documents with handwritten text and let Algodocs automatically extract data and convert it to Excel or JSON. Let’s consider the following portion of a scanned document, which contains a table of five columns filled with handwritten numbers. If you upload this image to your account at Algodocs, you will see the following output, which has 100% accuracy. Algodocs uses advanced ICR engines trained with Artificial Intelligence algorithms and Deep Learning. Extracting data from various document formats could be a challenging task, especially when it comes to the necessity to extract specific data sets from files containing different types of documents that span across multiple pages. In these quick materials, we will list key features that are available in Algodocs, and which will help you to extract data from your documents using the Algodocs advanced AI engine without relying on templates or even labeling and training your files. What Are the Supported File Formats for Data Extraction? You may upload to Algodocs different types of files of different Image formats for data recognition and data extraction: Portable Document Format (PDF) Joint Photographic Experts Group (JPEG) Portable Graphics Format (PNG) Tagged Image File Format (TIFF) What Are the Supported Languages for Data Recognition? Algodocs supports data extraction from documents with Arabic, Armenian, Belorussian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Filipino, Finnish, French, German, Greek, Hebrew, Hindi, Icelandic, Indonesian, Italian, Japanese, Korean, Lao, Latvian, Lithuanian, Macedonian, Nepali, Norwegian, Persian, Polish, Portuguese, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Telugu, Thai, Turkish, Ukrainian, Vietnamese, and many other up to 200 languages. How to Extract Handwritten Data from PDFs Using Algodocs Step 1: Log in to your Algodocs account and go to the home page which is the Dashboard. Step 2: Click on the Extractors tab, where you can see the Create button on the top right side, click on it. Step 3: Choose the Custom Extractor, for getting structured data from your documents as you need it. Step 4: A pop-up window will appear, upload your sample file to extract data from. Click on the Choose file, to locate the document from your device storage folder, then assign a name to the extractor. Once done, click on the Create Extractor button. It will populate under Extractors as below; in this article, our example is called “Sample1.” Step 5: Click on the blue button labeled “Manage”, to create the data to be extracted. Step 6: Click on Add to choose what type of extraction method you want, here, you may use rule-based and AI extraction. In this example, we will choose the AI extraction method, “Form Data Extraction.” After clicking on “Form data extraction”, the page that you want to extract data from will appear on a new page. On the Top Right corner click on” Continue”. Step 7:  The raw data from your document is displayed. Now use available filters to select certain data, and update, or format the extracted data as you like. Once done, write the Field/Table name on the Left side inside the blank text box, and click the SAVE button on the right side. Step 8:  Now go to the extracted date and choose the extractor name, from the first drop-down menu. The extractor will populate the extracted date information. To view the extracted data, click on the Rows, and the data will populate as below.  Then choose to download the data as Excel, JSON, or XML Final Thoughts As we can see Algodocs performs well in handwritten text extraction from scanned documents. Feel free to start a free subscription right now and test your handwritten scanned documents. You can use Algodocs for free forever, and you will have 50 pages per month. If you need to process a higher number of pages, then please see our affordable pricing plans. Please contact

Data Extraction, PDF

What is a PDF Parser?

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Back to Blog Table of contents On this page What is a PDF Parser? Home › Blog › Data Extraction › What is a PDF Parser? Categories Data Extraction PDF Tags ai platform for data extraction algodocs character recognition data extraction from images deep learning export extracted data to excel invoice process machine learning ocr api web-based ai platform for data extraction By Shubhankar Biswas Published October 13, 2025, 12:03 Updated July 29, 2026, 02:32 A PDF Parser is a program or a library that enables end-users and organizations to parse data from native PDF documents. Often, organizations need to parse PDF documents for specific fields such as Account Number, Date, Address, Bill to/from information, or parse tabular data. PDF Parsers are usually needed and used for processing and parsing data from large amounts of documents. On the other hand, when you have a handful of documents you simply go and copy the data you need from PDF documents manually and paste it to Excel or anywhere you need it to be. PDF Parsers enable end-users to get data from hundreds and thousands of PDF documents in real time by saving huge amounts of time and, thus, money. Parsing pdf documents isn’t an easy task. There are various ways native pdf documents are generated and parsing data from such pdf documents requires smart approaches. Parsed data from PDF documents greatly varies depending on the industry, which means the data parsed might also greatly change, which complicates the task. Parsing PDF medical forms, which contain specific fields such as First, Middle, and Last names, Sex, Date of Birth, etc. are very different from PDF purchase orders that contain mainly the items in the tabular form with such columns as Item No, Code, Quantity, Item Price, Amount, etc. Therefore, if PDF Parser produces just a bunch of text from a PDF document it does not make much sense for the end-user. What end-users or organizations require is the structured data parsed from PDF documents. In other words, PDF Parser should extract from PDF documents only the data the end-user needs and in the right structured format. For this, the PDF Parser must be smart and flexible enough to parse PDF documents with various layouts and data types. How to parse PDF documents with various layouts? Algodocs allows you to parse PDF documents of any complexity in their layouts and type of data. With the flexible extracting rules of Algodocs, you can parse data from PDFs with different layouts. It is very easy and quick to set up extractors in Algodocs for your PDF documents. We provide 100% free technical support and are ready to set up extractors for you. While we provide free support for creating extracting rules, you may check our help and support section if you wish to learn how to create extracting rules in Algodocs. Watch the following introductory video to get an idea of how it works in Algodocs. Feel free to start a free subscription right now and parse your pdf documents. You can use Algodocs free forever with 50 pages per month. If you need to process a higher number of pages, then please see our affordable pricing plans. If you have specific requirements and need a custom solution, please contact us. You Might Also Like PDF Data Extraction: The Best Tool and Techniques PDFs are ubiquitous in every organization, serving as the go-to format for sharing and exchanging business data. However, extracting, editing, or… Shubhankar Biswas October 13, 2025 PDF Image Extraction: A Comprehensive Guide To Extracting Image Data From Scanned Pdf Files In 2025 PDF image extraction is a challenging process. Without proper tools and technology, this process can be tedious and prone to errors, which can… Shubhankar Biswas September 1, 2025 Convert PDF to JSON – Convert PDF Documents to Structured JSON Objects Table of Contents Introduction Organizations in various industries widely use PDF documents, since no doubt PDF is a common document format for… Shubhankar Biswas October 13, 2025 Start extracting your data today No credit card required. Process up to 50 pages per month on the Forever Free plan. Book a Demo Get Started for Free

PDF

Convert PDF to JSON – Convert PDF Documents to Structured JSON Objects

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Back to Blog Table of contents On this page Convert PDF to JSON – Convert PDF Documents to Structured JSON Objects Home › Blog › PDF › Convert PDF to JSON – Convert PDF Documents to Structured JSON Objects Categories PDF Tags ai platform for data extraction algodocs character recognition data extraction from images deep learning export extracted data to excel invoice process machine learning ocr api web-based ai platform for data extraction By Shubhankar Biswas Published October 13, 2025, 11:59 Updated July 21, 2026, 05:15 Introduction Organizations in various industries widely use PDF documents, since no doubt PDF is a common document format for businesses to transfer data. Purchase orders, Invoices, Agreements, and many more document types are interchanged in PDF formats. On the other hand, JSON is another format that represents data in a structured format, which is widely used in transferring data between web applications. As a result, Working with JSON is much easier than with PDF. Therefore, in this article, we will talk about PDF and JSON formats and how you can convert your PDF documents to JSON format. What is a PDF? PDF (Portable Document Format) was initially developed by Adobe® Systems in 1992 and is standardized as ISO 32000. What makes PDF so popular is it is independent of the application software, hardware, and operating system. Other than text and images PDF files may contain a variety of content such as annotations, form fields, layers, etc. There are many advantages of PDF format such as multi-dimensionality, which we have already mentioned – being able to contain various types of content, text, images, videos, vector graphics, interactive fields, hyperlinks, and buttons. Moreover, PDF documents are easily created and viewed on different devices.  Security in PDF was one of the primary concerns of Adobe® Systems. Therefore, PDFs have different access levels to protect the content and the whole document, such as passwords, digital signatures, and watermarks. However, some of the downsides of a PDF are the complexity of editing and especially extracting data from it. Moreover, PDFs are not generated in the same way, so different PDF files can be created in various ways, which complicates the task of extracting data from PDF documents. What is a JSON? JSON (JavaScript Object Notation) is a very popular data format, which appeared in the early 2000s. JSON is a language-independent data format and is used to transfer data between software applications, particularly web applications, usually between server and client.  Most of the API integrations are realized using JSON format for data transfer since it is very easy to work with JSON. Consider a JSON object called person, which contains the following information:{   “name”: “John”,   “surname”: “Doe”,   “age”: 25} Accessing fields of a JSON object is as simple as using the name of the object and the field name you want to access by separating them with a dot as follows: To access a person’s name we use person.name, which will give us “John” as a result. Similarly, we do for surname and age fields: person.surname, person.age Note how easy it is to access any field of a JSON object, which is definitely not compared to accessing specific information in the PDF document. How does JSON differ from PDF? Although PDF and JSON are both widely spread and used, there is a huge difference between PDF and JSON. The difference between them is simply in the purpose of their usage. PDF is mainly used for exchanging information between humans, since it contains text, graphics, illustrations such as images and videos, etc. On the other hand, JSON is mainly used between computer programs and different applications for communicating and exchanging data between each other. It is not an easy task for a human to read information from a JSON file, especially if it is a compressed one, but it is a perfect way to access information from JSON for a software application. The opposite goes for the PDF. Therefore, PDF and JSON become important, useful, and helpful only when they are used in the right place and for the right purpose. How to Convert PDF to JSON? Often, organizations need to transfer data to other programs for further processing. This data is often stored in PDF documents since businesses often speak to each other in a “PDF language”. However, extracting information from PDF documents can be challenging.  The simplest solution is that you can always copy and paste text from a PDF and send it to where it belongs. However, this simple approach has many problems, since first of all this will work only with native PDF files (not scans) for which you can even use some free PDF Parsers. Another problem even if your PDF documents are all native, it is not easy to copy the entire table from a PDF by maintaining its format, especially if the table spans over multiple pages, for example, 100 or 1000 pages. Additionally, often organizations need to extract specific data from PDFs, for example not the entire table, but instead specific rows or columns based on some conditions. Last, but not least, it is not worth spending your valuable time on menial data entry! Convert PDF documents to JSON with Algodocs Algodocs offers a perfect solution to extract any type of data from PDF documents and transfer it to other programs in real-time. Algodocs can extract fields and tables of any complexity from native as well as scanned PDF documents. You can convert your PDF documents to JSON in three steps with Algodocs. First, start by creating an extractor in Algodocs. Algodocs has some preprocessing operations that take some time depending on the number of pages your PDF document contains; usually, it is around 15-20 seconds. Then, go to the ‘Extracting Rules’ editor to create your extracting rules for every field you need to extract from your PDF documents. Similarly, if you need to extract tables from your PDF documents you can create extracting rules for tables by selecting ‘Table’ as the data type. After you are done with creating and extracting rules,

Algodocs

Extract handwritten text from scanned PDFs and images

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Back to Blog Table of contents On this page Extract handwritten text from scanned PDFs and images Home › Blog › Algodocs › Extract handwritten text from scanned PDFs and images Categories Algodocs Tags ai platform for data extraction algodocs character recognition data extraction from images deep learning export extracted data to excel invoice process machine learning ocr api web-based ai platform for data extraction By Shubhankar Biswas Published October 13, 2025, 11:56 Updated November 10, 2025, 13:34 Optical Character Recognition (OCR) engines are primarily focused on machine-printed text and may produce low accuracy for handwritten text. Intelligent Character Recognition (ICR) is an advanced recognition system that is used to recognize handwritten text. This allows the automatic conversion of text in an image into letter codes that are usable within computer and text-processing applications. Although many processes involve computer-based operations and are implemented in a digital environment, paper is still vastly used across most core business processes such as mortgage origination, order fulfillment, contracts, and other documents that usually require handwritten input and signatures. Nowadays, the digitalization of paper documents plays an important role, and deciding on the right data capture software is critical since handwriting recognition unlike printed text recognition is a more complex task that usually involves advanced deep learning algorithms. Algodocs: Deep Learning Handwriting Recognizer Algodocs is capable of converting handwritten text into machine-printed text with high accuracy. With the ICR of Algodocs, you can automate your document processing workflow and get rid of manual data entry. Scan your paper documents with handwritten text and let Algodocs automatically extract data and convert it to Excel or JSON. Let’s consider the following portion of a scanned document, which contains a table of two columns filled with handwritten numbers. If you upload this image to your account at Algodocs, you will see the following output, which has 100% accuracy. Algodocs uses advanced ICR engines trained with Artificial Intelligence algorithms and Deep Learning. The above example includes mostly digits. Another example with characters is given below. The following is the extracted text by Algodocs from the above image. As we can see Algodocs performs well in handwritten text extraction from scanned documents. Feel free to start a free subscription right now and test your handwritten scanned documents. You can use Algodocs free forever with 50 pages per month. If you need to process a higher number of pages, then please see our affordable pricing plans. If you have specific requirements and need a custom solution, please contact us. You Might Also Like How to Extract Handwritten Data from PDFs with Algodocs? Table of Contents Want to learn how to extract handwritten data from PDFs easily for your business documents? Algodocs is a powerful AI platform… Shubhankar Biswas October 13, 2025 Conquer Multilingual Data Extraction: A Step-by-Step Guide with Algodocs Introduction Sick of spending hours manually extracting data from multilingual PDFs and images? It could be more efficient, prone to mistakes, and,… Shubhankar Biswas October 12, 2025 Why convert PDF to Word on MacBook (Mac)? Picture this: You have a PDF file that needs to be revised or a scanned document that you wish to convert into an editable format like an MS Word… Shubhankar Biswas October 12, 2025 Start extracting your data today No credit card required. Process up to 50 pages per month on the Forever Free plan. Book a Demo Get Started for Free

Image Data Extraction

A Guide on Extracting Tables From Low-Quality Scanned Documents

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Back to Blog Table of contents On this page A Guide on Extracting Tables From Low-Quality Scanned Documents Home › Blog › Image Data Extraction › A Guide on Extracting Tables From Low-Quality Scanned Documents Categories Image Data Extraction Tags ai platform for data extraction algodocs character recognition data extraction from images deep learning export extracted data to excel invoice process machine learning ocr api web-based ai platform for data extraction By Shubhankar Biswas Published October 13, 2025, 11:52 Updated July 22, 2026, 09:54 Many companies deal with thousands of documents every month. Document workflow automation becomes vital for such companies as the number of documents increases. One of the most frequent and at the same time tedious operations when processing documents is reading data from tables, especially when documents are scanned PDFs or images. Automating table extraction from scanned documents and exporting them into Excel or JSON within seconds is a dream for every company dealing with manual data entry. Automating table data extraction from scanned documents and images reduces operational costs and saves a lot of time. In this article, we will talk about table extraction from scanned documents or images with low quality. You, most probably, came across some online tools that can extract tabular data from documents. However, there are a few that really work with low-quality scanned documents or images taken by a mobile device. Optical Character Recognition (OCR) is the technology used for converting scanned images into text. However, standard OCR tools require you to apply certain image processing operations on the images before you can apply OCR on them. Without manual pre-processing, OCR will fail in most cases, and accuracy will be low. Unfortunately, even with pre-processing operations free OCR tools produce poor performance. How to extract tables from scanned PDFs and images with low quality? Algodocs has an advanced AI-powered OCR engine that automatically handles any type of scanned PDF or image with a low quality. Algodocs accepts either colorful scanned images, black and white, or any other settings and extracts data with high accuracy. Algodocs can process scanned images with as low a dpi as 75. If you have scanned PDFs or images with low quality, then Algodocs is the right solution for you. You may start a free subscription right now and test your own scanned documents since we offer a free subscription (forever) with 50 pages per month. If you need to process a higher number of pages, then please see our affordable pricing plans. Please read our article on the basic steps for table extraction from documents here: Extract tables from PDF and scanned documents Algodocs: the best software tool to extract tables from scanned PDFs and images Consider the portions of the scanned documents below and the tables that Algodocs extracted from them. Example #1 Sample scanned image with low-quality (black and white) Extracted table by Algodocs. Example #2 Extracted table by Algodocs As you can see, the accuracy of Algodocs is perfect even with low-quality scans. However, there are cases when scanned images may cause Algodocs to make mistakes concerning small characters such as punctuation or other symbols (points, commas, date separators, etc.). Let’s have a look at the example below with a scanned image and see what Algodocs could extract from it. The extracted table from the above-scanned image is shown below. As you can see, there are numbers that are extracted with wrong decimal separators (indicated in red circles), i.e. a decimal point is mistakenly recognized as a comma. This is due to the dark background that some rows have on the image. With the help of flexible extracting rules of Algodocs, the workaround is quick and simple. Whenever you have low-quality scanned PDFs of images, we always advise you to follow the steps explained below. Step1. Remove all points and commas from the numbers We apply the ‘Search & Replace’ filter in Algodocs by using regular expressions as the search type. We apply this rule to all the columns in the example below, but you can restrict this rule to a specific column when needed. In order to find all dots or commas we use .|, as the search term and we leave empty the second field (replace by this), since we simply want to remove them. Step 2. Convert all numbers to their previous format Since we removed all points and commas from numbers, they actually increased, i.e. multiplied by 100 we can say (2,378.63 became 237863). Therefore, since we know that our numbers had 2 decimal places, we can divide all numbers by 100 to get the original numbers. The ‘Arithmetic Operation’ filter helps us implement exactly this. We divide numbers by 100 in the last column as shown in the example below. You may apply this filter to other columns too. That’s it. We got numbers in their original form with 100% accuracy! The same approach can be applied to other symbols when you have documents with a low quality. Please, contact us if you need any assistance. You Might Also Like How to Extract Handwritten Data from PDFs with Algodocs? Table of Contents Want to learn how to extract handwritten data from PDFs easily for your business documents? Algodocs is a powerful AI platform… Shubhankar Biswas October 13, 2025 Data Extraction for Legal Industry : How Intelligent Document Processing (IDP) Can Transform Legal Industry Document Workflow The global law and legal services industry is expected to reach $1,591.56 billion by the end of 2032, according to a report. The legal industry… Shubhankar Biswas October 12, 2025 Extract Tables from Images with AlgoDocs Extract Tables from Images with AlgoDocs One might find themselves overwhelmed by a deluge of paperwork—orders, checks, articles—all containing… Ibrahim Nalbant June 20, 2024 Start extracting your data today No credit card required. Process up to 50 pages per month on the Forever Free plan. Book a Demo Get Started for Free

Algodocs

Top 5 Data Extraction Tools for 2025: Streamline Your Document Processing With AI

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Back to Blog Table of contents On this page Top 5 Data Extraction Tools for 2025: Streamline Your Document Processing With AI Home › Blog › Algodocs › Top 5 Data Extraction Tools for 2025: Streamline Your Document Processing With AI Categories Algodocs Tags ai platform for data extraction algodocs data extraction from images invoice process ocr api By Shubhankar Biswas Published October 12, 2025, 18:57 Updated July 20, 2026, 04:07 Harnessing Advanced Technologies for Efficient Data Management Data processing is the backbone of modern businesses. As companies adopt cutting-edge technologies to boost efficiency, data extraction and storage become crucial in shaping their future. Whether you’re a large enterprise or a small business, handling substantial data is inevitable. Relying on manual methods is not only slow but also prone to errors, which can significantly impact your business’s efficiency and lead to potential losses. Thankfully, advancements in technology, particularly OCR (Optical Character Recognition) applications, have made data extraction much simpler and more accurate. The rise of artificial intelligence and machine learning has further enhanced OCR capabilities, making it incredibly easy to extract data from a wide range of documents, including blurry invoices and bills. In this blog post, we’ll explore the top 5 data extraction tools of 2025 that can help you efficiently manage your document data. Algodocs Best Data Extraction tool Algodocs is an advanced AI-powered data extraction platform designed to automate data extraction from PDFs, scanned images, and handwritten notes. When discussing the most reliable data extraction tools of 2025, Algodocs tops the chart With Algodocs, you can extract data from BOL documents, invoices, passports, ID cards, medical bills, or any type of document easily. Built with intelligent AI and ML algorithms, Algodocs can capture and extract data from blurry images, unstructured documents, and complex handwritten notes at 10X faster speed compared to any traditional or modern OCR apps.  The prebuilt extractors enable users to extract data from documents without any complex and lengthy setup. You don’t need any technical knowledge to use the Algodocs app. With a very intuitive and easy UI, Algodocs can be used by anyone from any background.  The seamless third-party app integration with Algodocs makes it one of the best choices for businesses to use as an AI tool. You can integrate Algodocs with Zapier, Google Docs, FTP, and other platforms for data integration.  Pros of Algodocs  High Accuracy: Algodocs utilizes advanced AI & ML algorithms to achieve over 99.99% accuracy in data extraction. This precision ensures minimal errors and reliable data processing, making it ideal for handling critical documents.  Ease of Use: The platform offers an intuitive and user-friendly interface, making it accessible even for non-technical users. The prebuilt extractor makes it super easy to use Algodocs for document processing. You don’t need extensive coding knowledge to create an extractor in Algodocs. With just a few clicks, you can extract data from a document easily.  Versatility: Algodocs supports a wide range of document types, including PDFs, Word documents, images, and handwritten notes. This versatility allows businesses to streamline various document processing tasks with a single tool.  API Integration: Algodocs offers a straightforward API for seamless integration with existing business systems and workflows. This integration capability enhances efficiency and eliminates the need for manual data entry. You can easily integrate Algodocs with third-party tools such as Zapier, SharePoint, Dropbox, REST API, and other services without paying extra costs.  Customer Support: Algodocs provides excellent customer support, assisting users in every possible way, even for small bundles. This support is invaluable for troubleshooting and learning best practices.  Affordable Pricing: Algodocs comes with very affordable and flexible pricing plans so that every business segment can utilize Algodocs features. Our basic plan starts at $23 USD per month and includes 300 monthly credits. You have access to all the integrations and other benefits in the plan, so you don’t need to pay extra for any integration or add-on services. You can also customize a plan as per your requirements. With lots of features and benefits, we can say that Algodocs is a great tool for the list of top data extraction tools of 2025. Cons of Algodocs Algodocs is a powerful tool for businesses looking to automate their document processing and improve efficiency. However, it’s essential to consider the potential drawbacks and evaluate whether it meets your specific needs and budget.  Docparser Docparser is an ideal document processing tool for converting PDFs and extracting data from various types of documents. It can be a great platform for medium and large businesses looking for data extraction and automation solutions. Pros Docparser excels in its ability to extract data accurately from documents. It is a great tool for all types of data extraction tasks, including processing different types of documents and invoices. The app’s accuracy is excellent, and the platform offers pre-built extractor options, allowing users to easily set up and extract data from documents. Additional add-ons such as parsing assistance, multi-factor account authentication, version control, and other features make it a highly capable data extraction product. Cons While Docparser offers excellent features, one of its biggest drawbacks is pricing. The basic plan starts at $30 USD, which can be expensive for some users. Additionally, extra charges apply for using any additional add-ons or features, which further increases the cost and could be a significant downside for the platform. Docsumo Docsumo is a powerful data extraction tool that allows you to easily extract information from documents and invoices. It includes features like automatic document classification, analytics, and batch processing, enabling efficient data capture and extraction from a wide range of document types. Pros One of the most highlighted features of Docsumo is its batch processing and accuracy in data extraction. With its robust capabilities, Docsumo is an excellent option for large businesses looking for reliable document processing solutions. Cons One of the biggest drawbacks of Docsumo is its pricing model. The pricing starts at $299 USD per month, which only includes 1,000 credits per month. This limitation can be a significant disadvantage for businesses with higher processing needs. Nanonets Nanonets is a feature-rich document

ID Card Data Extraction
Algodocs

ID Card Data Extraction: Transforming Identity Verification with AI and OCR In 2025

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Back to Blog Table of contents On this page ID Card Data Extraction: Transforming Identity Verification with AI and OCR In 2025 Home › Blog › Algodocs › ID Card Data Extraction: Transforming Identity Verification with AI and OCR In 2025 Categories Algodocs Tags ai platform for data extraction algodocs character recognition data extraction from images id card data extraction machine learning ocr api By Shubhankar Biswas Published September 1, 2025, 09:46 Updated July 10, 2026, 11:57 ID cards are crucial for individuals and corporate organizations for various reasons. In today’s fast-paced digital world, businesses and organizations need efficient ways to extract data from various types of identity cards for KYC, security, compliance, and customer onboarding. However, manual data entry is slow and prone to errors. This is where Artificial Intelligence (AI) and Optical Character Recognition (OCR) come into play, making the process faster, more accurate, and seamless across different industries. This blog explores the essentials of ID card data extraction, how it works, the technologies behind it, its real-world applications, challenges, and how Algodocs AI is leading the way in intelligent data extraction. What is ID Card Data Extraction? ID card data extraction involves capturing and extracting information from various types of identity documents, such as passports, driver’s licenses, national ID cards, employee badges, and student IDs. These extracted data are later used for identity verification, KYC (Know Your Customer), automated form-filling, and record management in different organizations. Businesses use ID card data extraction to enhance security, improve and streamline customer interactions, and boost operational efficiency. Some of the key data points extracted from an ID card include: Full Name Date of Birth Address Identification Number Expiry Date Issuing Authority Signature QR Codes or Barcodes Biometric Data (such as Photos) ID card data extraction is a multi-step process designed to deliver accurate and efficient results. It begins with capturing a clear image of the ID card using a scanner, smartphone camera, or document upload. Next, AI enhances the image through preprocessing by improving contrast, reducing noise, and correcting any distortions or angles. OCR technology then detects and extracts the text from the image, followed by AI-powered algorithms that organize the extracted text into structured fields like name, date of birth, and ID number. The data is then verified by cross-checking it against predefined parameters to ensure accuracy. Finally, the structured data is securely stored or seamlessly integrated into business applications, making it readily available for use with precision and reliability. What Types of ID Card and what types of data Can Be Extracted with AI and OCR? You can extract data from passports, driving licenses, and corporate ID cards using AI OCR apps. The following information can be extracted with ID card OCR apps, including: Personal Details: Full name, gender, date of birth. Document Information: ID number, issue and expiry date. Address Information: Residential or office address. Biometric Data: Signatures and photos. Security Features: Watermarks, holograms, QR codes, and barcodes. Organizational Details: Employer name, student ID, and membership numbers. Signup for Algodocs AI free ID card data extraction app today and access all the paid features for free. Sign Up Now Extracting these details helps businesses with verification, compliance, fraud prevention, and automated onboarding. Technology Behind ID Card Data Extraction: OCR & AI To achieve high accuracy and efficiency, ID card data extraction relies on a combination of Optical Character Recognition (OCR) and Artificial Intelligence (AI). These technologies work together to automate the process of identifying, extracting, and digitizing data from ID cards, eliminating the need for manual data entry and reducing human errors. How OCR Works for ID Card Data Extraction Optical Character Recognition (OCR) is a sophisticated technology designed to scan and convert printed or handwritten text into machine-readable digital data. It is the backbone of ID card data extraction, enabling businesses, financial institutions, and government agencies to streamline identity verification and document processing. The OCR Process for ID Card Data Extraction OCR follows a structured workflow to ensure accurate and efficient extraction of text from ID cards. The process involves multiple steps, as outlined below: Capturing an Image of the ID Card The process begins with scanning or photographing the ID card using a scanner, mobile camera, or document imaging device. High-quality images with proper lighting and minimal glare improve OCR accuracy. Image preprocessing techniques, such as noise reduction, contrast enhancement, and skew correction, are applied to improve readability. Detecting Text Regions Using AI AI-powered OCR systems analyze the scanned image to identify and segment text areas from graphical elements such as logos, watermarks, and holograms. Machine learning models help differentiate between text and non-text elements, ensuring only relevant data is extracted. Recognizing Character Patterns & Extracting Data OCR algorithms process each text segment, identifying individual characters, numbers, and symbols. AI-powered pattern recognition improves accuracy, allowing the system to recognize different fonts, languages, and even stylized text. Some advanced OCR solutions incorporate Intelligent Character Recognition (ICR), enabling the extraction of handwritten text. Converting the Text into a Structured Digital Format The extracted text is converted into structured digital formats such as JSON, CSV, XML, or databases. This allows businesses to easily integrate ID card data with customer management systems, banking applications, HR platforms, and other databases. Validating Extracted Data for Accuracy AI-powered post-processing techniques are used to validate and correct any potential errors in the extracted text. This step ensures that information such as names, ID numbers, and expiration dates are correctly interpreted and formatted. AI models compare extracted data with predefined templates, helping detect discrepancies or inconsistencies. Capabilities of Modern OCR Solutions With the rapid advancements in AI and machine learning, modern OCR solutions have evolved to offer greater accuracy and versatility. Some key capabilities include: Multi-Language Recognition: OCR can extract data from ID cards in various languages, including complex scripts such as Arabic, Chinese, and Cyrillic. Support for Different Fonts & Formats: Advanced OCR models can handle various font styles, text orientations, and formatting styles commonly found in global ID cards. Handwriting Recognition (ICR): Intelligent Character Recognition (ICR) enables the extraction of handwritten details, such as signatures or handwritten notes on IDs. Fraud Detection & Security Enhancements: AI-driven OCR can detect altered or fraudulent

Algodocs

Automating Loan Document Data Extraction with Intelligent Document Processing: A Case Study

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Back to Blog Table of contents On this page Automating Loan Document Data Extraction with Intelligent Document Processing: A Case Study Home › Blog › Algodocs › Automating Loan Document Data Extraction with Intelligent Document Processing: A Case Study Categories Algodocs Tags algodocs data extraction from images IDP loan forms machine learning By Shubhankar Biswas Published September 1, 2025, 09:06 Updated July 20, 2026, 03:52 The process of applying for a home loan, car loan, education loan, or any type of personal or business loan is a lengthy one. It involves extensive documentation, verification, validation, screening, and ultimately, loan approval. Numerous documents are essential in this process, such as loan forms, identity documents, tax forms, and more. However, capturing, extracting, and organizing data from each individual form presents a significant challenge. Manually extracting data from diverse document types and formats, then effectively sorting, refining, and applying that data for real business tasks, is an additional hurdle. Thankfully, AI and Intelligent Document Processing have made these tasks more accessible, accurate, and efficient for various financial institutions that offer different types of loan services to their customers. In this blog, we will explore how banks and financial institutions can leverage Artificial Intelligence and Intelligent Document Processing to enhance the speed and accuracy of loan document data extraction. We will also discuss why modern businesses are increasingly investing in these technologies to automate data extraction for loan documents. Before diving into the main topic, let’s first understand what a loan document is, the types of loans, the documents involved in processing a loan, the types of data businesses need to extract from these documents, and then we will delve into the core topic. Understanding Loan Document Data Extraction In 1959, when Barclays Bank in Britain first used a computer for banking purposes, computer technology has since made significant strides in various types of banking and financial activities. Among these, loans and mortgages are some of the financial activities that have seen complete transformation. Loan document data extraction is a critical part of modern banking where computers, Artificial Intelligence, and Intelligent Document Processing are enhancing the efficiency of this process. Essentially, loan document data extraction refers to the process of identifying and capturing relevant information from financial documents. These documents come in various formats, including PDFs, scanned images, and handwritten notes. Manually extracting crucial details such as borrower information, loan amounts, interest rates, repayment terms, and supporting financial data can be difficult due to human errors, lack of speed, inaccuracy, and slow processing times. However, with the rise of AI-driven IDP solutions, businesses can now automate this process, reducing manual effort and improving data accuracy. By utilizing technologies such as Optical Character Recognition (OCR), Natural Language Processing (NLP), and Machine Learning (ML), IDP tools can extract both structured and unstructured data, validate it, and integrate it seamlessly into loan processing systems. The Role of Intelligent Document Processing in Loan Data Extraction Intelligent Document Processing (IDP) is revolutionizing how financial institutions handle loan documents and extract data from them. IDP combines AI and ML with traditional data extraction techniques such as OCR to extract, classify, and validate data in real-time from a loan document. Here’s how IDP enhances loan document data extraction: Automated Data Capture: IDP eliminates the need for manual entry by automatically capturing key data fields from loan documents. This results in increased work efficiency, saving both time and cost. Additionally, you can integrate IDP with third-party platforms to make loan data extraction even more useful. Improved Accuracy: Advanced AI and ML algorithms reduce errors that occur in manual processing, ensuring higher data integrity. In manual data extraction, reliance on humans for evaluation and extraction of data often leads to errors. In contrast, automated AI-based IDP tools make fewer or no errors in data extraction. Faster Loan Processing: Automation accelerates document verification, reducing approval times and enhancing customer experience. On the other hand, manual document verification and processing take more time, eventually hampering the customer experience. Compliance and Security: IDP solutions ensure adherence to financial regulations and protect sensitive customer information. Manual data extraction, however, exposes valuable data to unauthorized access, which can lead to serious data breaches. Scalability: Businesses can process large volumes of loan documents efficiently without increasing operational costs. In contrast, manual data extraction requires more manpower to extract data from loan documents, resulting in higher operational costs and limited scalability. Challenges in Manual Loan Document Data Extraction Manual loan document data extraction presents several challenges that hinder efficiency, speed, accuracy, and cost for data extraction. Some of the key challenges include: Time-Consuming Process: Manual data entry is labor-intensive and time-consuming, leading to delays in loan processing and approval. Extracting data from each individual document takes more time, which slows the loan processing and approval timeline for borrowers. Prone to Errors: There is a famous quote by Alexander Pope, “To err is human,” which means humans are destined to make errors. Human errors are common in manual data extraction, which can result in incorrect data entry and compliance issues. High Operational Costs: Manually extracting data from loan documents requires more human power, leading to an increase in operational costs. You need to hire more people to extract data from loan documents, and then you also need to train them to do the task efficiently. This results in labor costs, operational costs, and additional expenses for the employer. Limited Scalability: As the volume of loan documents increases, manual data extraction becomes unsustainable and limits scalability. This requires more manpower to do the job, which leads to rising operational costs, decreased work efficiency, and other challenges. In short, manual loan document data extraction isn’t ideal in terms of scalability. Benefits of Automating Loan Document Data Extraction with IDP Implementing IDP solutions for loan document data extraction offers numerous benefits which can greatly improve loan processing, verification, and approval timeline. Below are the benefits of IDP for loan document data extraction: Try Algodocs AI for extracting data from any loan documents and achieve 10x speed and accuracy with our app. Try For Free Enhanced Data Management: Automated

Aadhar Card Data Extraction using AI
Algodocs

Aadhar Card OCR: Extracting Data From Aadhar Card Using AI

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Back to Blog Table of contents On this page Aadhar Card OCR: Extracting Data From Aadhar Card Using AI Home › Blog › Algodocs › Aadhar Card OCR: Extracting Data From Aadhar Card Using AI Categories Algodocs Tags ai platform for data extraction algodocs data extraction from images id card extraction id card ocr machine learning ocr api web-based ai platform for data extraction By Shubhankar Biswas Published September 1, 2025, 08:50 Updated July 20, 2026, 05:40   In today’s fast-paced digital era, automation and artificial intelligence (AI) have transformed data extraction processes across various industries. One of the most critical use cases in India is Aadhar Card data extraction, where Optical Character Recognition (OCR) and AI technology are used to extract data from Aadhar cards with higher accuracy. Since the Aadhar card serves as a crucial identity document (ID) for Indian citizens, automating data extraction can significantly enhance work efficiency in many industries such as finance, telecom, healthcare, and e-governance. With the growing need for faster and more reliable document processing, AI-powered OCR solutions (IDP) are becoming essential tools for businesses in India. By eliminating manual data entry, businesses can reduce errors, enhance efficiency, and improve data management. In this blog, we will explore how Aadhar Card OCR works, its advantages, and how AI-driven solutions like Algodocs streamline the data extraction process. So What is Aadhar Card OCR? Aadhar Card OCR is an Optical Character Recognition (OCR) technology used to capture and extract details from a scanned image or PDF format of an Aadhar card. The key details extracted through this process include the 12-digit Aadhar card number, the full name of the cardholder, date of birth, gender, address, and QR code data. Extracting this information manually can be time-consuming and prone to errors. However, an AI-based OCR app can easily capture, extract, and sort the data with the help of AI, allowing you to automate the entire process. The best part of AI-based OCR tools is that they improve accuracy, speed, and efficiency by using sophisticated algorithms that analyze, recognize, and extract text even from low-quality images, which is not possible with manual data extraction approaches. Unlike traditional OCR systems, AI-powered solutions are trained on vast datasets, allowing them to recognize different fonts, complex formats, and text placements with greater precision and speed. How AI Elevates  Aadhar Card Data Extraction Efficiency Traditional OCR solutions often struggle with poor-quality images, handwritten text, or complex document layouts. However, AI-powered OCR can overcome these challenges by using machine learning (ML), natural language processing (NLP), artificial intelligence (AI), and advanced image preprocessing techniques. Machine learning algorithms and artificial intelligence enable OCR tools to recognize and extract text from Aadhar cards with a high degree of accuracy. These algorithms are trained to learn from large volumes of data, allowing them to improve their performance over time. Natural language processing (NLP) helps in interpreting and structuring the extracted information correctly, ensuring that fields such as names and addresses are properly identified. Image preprocessing techniques further enhance accuracy by removing background noise, adjusting contrast, and correcting distortions in scanned or photographed documents. This ensures that even low-quality images can be processed with minimal errors. Another significant advantage of AI-powered OCR is fraud detection. The system can identify forged or tampered Aadhar cards by analyzing patterns and inconsistencies within the document, thus ensuring data authenticity. The Aadhar Card Data Extraction Process Extracting data from an Aadhar card using AI-based OCR involves several key steps. The process begins with image acquisition, where a scanned copy or photograph of the Aadhar card is uploaded; the file can be a PDF as well. The AI-driven OCR software then processes the image to enhance readability by removing any background noise and correcting distortions. Once the image is pre-processed, the OCR engine scans the document and recognizes the text using deep learning algorithms. The extracted data is then structured into predefined fields, ensuring that each piece of information, such as the Aadhar number, name, age, gender, and address, is correctly categorized. After the text is extracted, the system validates the information by cross-checking it with predefined data formats. Finally, the structured data is exported for integration into various applications, such as customer onboarding systems, KYC verification processes, and digital record management solutions. Benefits of AI-Powered Aadhar Card OCR platforms AI-powered Aadhar Card OCR apps offer numerous advantages over manual data entry or traditional OCR apps. One of the primary benefits of AI-powered Aadhar Card OCR apps is their enhanced data extraction accuracy and increased extraction speed. AI models trained specifically for Aadhar card data extraction ensure precision, minimizing errors that could occur due to manual input. This high accuracy is particularly beneficial for businesses that require large-scale data processing, such as banks, telecom companies, and government agencies. Try Algodocs AI-based Aadhar Card OCR for all types of ID card data extraction and KYC document data extraction work for free. Another key advantage is speed. The automation of data extraction significantly reduces processing times, allowing businesses to complete customer verifications, document submissions, and other administrative tasks much faster. This improved efficiency translates into cost savings, as organizations no longer need to rely on extensive human labour for data entry tasks. One of the most important benefits of AI-powered Aadhar Card data extraction is fraud prevention and data security, which are crucial factors in safeguarding personal information as the Aadhar card is an important ID card. Unauthorized access to this information can lead to many negative consequences for users and the organization. AI-powered OCR systems can detect forged or tampered documents by analyzing subtle inconsistencies in text alignment, font variations, and background patterns. This ensures that only genuine Aadhar card data is processed, enhancing security and compliance. Furthermore, AI-based OCR tools integrate seamlessly with existing systems. Whether it’s a CRM platform, banking application, or government database, the extracted data can be automatically transferred to ensure smooth workflow automation. Applications of Aadhar Card OCR Across Industries Though Aadhar Card OCR has a wide range of applications across

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