Comprehensive Guide on Optical Character Recognition (+6 Best OCR Software in 2024)

Traditionally, processing bank statements, pay slips, invoices, or contracts would mean hours of manual data entry, often accompanied by errors and delays. 

But what if you could reduce that process to a few seconds with near-perfect accuracy? Thanks to optical character recognition (OCR) technology, this is not only possible but increasingly common.

Optical Character Recognition (OCR) has become a savior for organizations looking to simplify operations and improve efficiency. Global Data reports that the OCR market will grow to $13.38 billion by 2026, driven by its ability to automate data extraction and significantly cut down on processing times. 

These are not just theoretical—real-world examples that demonstrate the transformative power of OCR.

Take Grid Finance, an Irish company specializing in income verification and lending. By integrating OCR into its workflow, Grid Finance could approve loans twice as fast and capture income data from bank statements and pay slips with a remarkable 93% accuracy.

The impact?

Over 93% of their processing is now touchless, saving them €5,000 per month in processing costs and freeing up more than 50 person-hours each month. 

These improvements have empowered Grid Finance to better support small and medium-sized businesses, enabling them to scale and thrive.

In this guide, we will walk you through the essentials of OCR—from understanding what it is and how it works to exploring the various types available and selecting the best software for your needs. 

Mastering OCR is no longer optional for businesses aiming to reduce operational costs and enhance data accuracy—it’s essential.

What is OCR (Optical Character Recognition)?

Optical Character Recognition (OCR) is a technology that converts various documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable data. 

It enables computers to read text from these documents and images, replicating the human reading process faster and more accurately.

1. How does OCR Operate?

OCR recognition analyzes an image's light and dark areas to identify text characters. It then matches these characters to a database of patterns, allowing the software to recognize and convert them into digital text. 

This text can then be manipulated, searched, or archived according to the user's needs. 

The technology can handle both printed and handwritten text. However, its accuracy may vary depending on the original document's quality and the OCR software's sophistication. 

• Automatic Data Entry: OCR often automates data entry from forms or invoices. For example, a company that processes hundreds of monthly invoices can use OCR to automatically extract essential information such as the invoice number, date, and amount due, thereby eliminating manual data entry. It leads to significant time savings and reduces the risk of human error.
• Document Digitization: Libraries, archives, and other institutions with extensive collections of printed materials often use OCR to digitize their collections. For instance, an archive might use OCR to convert historical newspapers into searchable digital files, making it easier for researchers to find specific articles or information without sifting through physical copies.
• Enhanced Accessibility: OCR also plays a critical role in making printed materials accessible to individuals with visual impairments. For example, OCR can convert printed books into digital formats that screen readers can read aloud or print in Braille, ensuring more people can access the information.

2. OCR in Business Operations

OCR is more than just a tool for document digitization. It’s a catalyst for operational efficiency across various sectors. For example, companies like Grid Finance have integrated OCR to streamline income verification processes in the finance industry.

The ability to transform physical documents into digital data is revolutionizing industries. Businesses can now access and analyze vast amounts of information in a fraction of the time it would have taken with manual processes. 

As organizations increasingly turn to digital solutions, OCR stands out as a fundamental technology that supports automation, data management, and even AI-driven insights.

How Does OCR Technology Work?

The Optical Character Recognition (OCR) process involves several stages, each contributing to accurately converting printed or handwritten text into digital data.

Understanding these stages helps to appreciate how OCR transforms static documents into editable and searchable content. 

Here’s a detailed look at how OCR works:

1. Image Acquisition

The first step in the OCR process is obtaining a digital image of the document or text that needs to be converted. This image can be captured in various ways:

• Scanning: Traditional scanners digitize physical documents, turning printed pages into high-resolution digital images.
• Photography: Smartphones or digital cameras can capture images of documents, especially when scanning equipment is unavailable. This method is commonly used for quick, on-the-go digitization.
• Digital Files: OCR can also be applied to digital documents like PDFs, where the text is locked in image format.

The quality of the acquired image significantly impacts the OCR process. Higher-resolution images with clear, sharp text produce better recognition results.

2. Preprocessing

Before the OCR software recognizes text, the digital image undergoes a series of preprocessing steps to enhance readability and accuracy. This stage is crucial for improving the success rate of text recognition:

• Noise Reduction: Extraneous marks, smudges, or background patterns are removed from the image to prevent the software from misinterpreting them as text.
• Binarization: The image is converted into a binary format (black and white) to contrast the text and the background. This simplifies the text recognition process by focusing on the core elements.
• Skew Correction: The software automatically corrects the skew to ensure the text lines are horizontal if the document was scanned or photographed at an angle.
• Segmentation: The image is segmented into distinct regions, such as text blocks, images, and tables. This helps the software focus on relevant text areas and avoid non-text elements.

These preprocessing steps ensure the text is as clean and legible as possible before the recognition phase begins.

3. Text Recognition

Text recognition is the heart of the OCR process. This stage involves analyzing the preprocessed image to identify and interpret individual characters, words, and lines. There are two primary methods used in text recognition:

• Pattern Recognition: The software compares the shapes of the characters in the image with a database of known patterns or templates. This method effectively recognizes printed text with consistent and predictable characters in standard fonts.
• Feature Extraction: In cases where the text is handwritten or uses unusual fonts, pattern recognition alone may not be sufficient. The software then employs feature extraction, breaking each character into distinguishing features like lines, curves, and intersections. The software can accurately identify characters by analyzing these features even when they vary from the norm.

During this stage, the software also accounts for font size, style, and orientation variations, ensuring that the recognized text is accurate regardless of how it appears in the original document.

4. Post-Processing

After the text is recognized, the software performs post-processing to refine the output and correct errors. This stage is essential for ensuring that the final text is both accurate and true to the original document’s formatting:

• Contextual Analysis: The software evaluates the recognized text in context, using linguistic algorithms to identify and correct errors based on grammar, syntax, and common word usage.
• Spell Checking: Misspelled or misrecognized words are flagged and corrected automatically during the text recognition phase.
• Formatting Preservation: The software preserves the original layout, including font styles, sizes, and text alignment. This ensures that the digital output closely resembles the original document, which is especially important for contracts, invoices, and forms.

5. Output Generation

The final stage in the OCR process is generating the output. The recognized text is converted into a digital format that can be edited, searched, or stored:

• Editable Documents: The text can be exported to word processors like Microsoft Word or Google Docs, which can be edited and formatted as needed.
• Searchable PDFs: OCR can create PDFs with embedded text layers, allowing users to search for specific terms or phrases within the document.
• Data Extraction: In some cases, OCR can extract specific fields, such as names, dates, or amounts, which can then be integrated into databases or spreadsheets for further analysis.

Workflow Example: Streamlining Invoice Processing

Consider a business that receives hundreds of invoices every month. The traditional approach would involve manually entering the invoice details into their accounting system—a time-consuming and error-prone task. With OCR, the invoice process becomes vastly more efficient:

• Image Acquisition: Invoices are scanned or photographed to create digital images.
• Preprocessing: The images are cleaned, binarized, and aligned to ensure clear, legible text.
• Text Recognition: OCR software extracts critical information, such as the invoice number, date, vendor name, and total amount, by recognizing and interpreting the text.
• Post-Processing: The extracted data is checked for accuracy, and any formatting, such as tables or line items, is preserved.
• Output: The data is automatically entered into the accounting system, eliminating the need for manual input and reducing processing time by over 90%.

This example illustrates how OCR not only automates data entry but also ensures that the output is accurate and ready for immediate use, making it a vital tool for businesses looking to improve efficiency.

What Are the 6 Types of OCR?

Optical Character Recognition (OCR) technology is not a one-size-fits-all solution. Various types of OCR have been developed to address specific challenges in text recognition, each offering unique strengths depending on the application. 

Below, we delve deeper into six types of OCR, explaining their mechanisms and providing detailed examples to illustrate their practical uses.

1. Pattern Recognition OCR

Pattern Recognition OCR is one of the earliest and most widely used forms of OCR. This method works by comparing the shapes of characters in an image with pre-stored templates or patterns of characters. 

The software has a database of these patterns, which it uses to identify matching characters in the scanned text. This method is particularly effective for recognizing printed text in documents with consistent fonts and styles.

For example, consider a government agency tasked with digitizing millions of old forms that were filled out using a typewriter. The agency uses Pattern Recognition OCR because the text on these forms is uniform, with a consistent typeface across all documents. 

The OCR software quickly scans each form, matches the characters with its database of patterns, and converts the text into a digital format. Since the forms use a standardized font, the software achieves high accuracy, ensuring the digitized documents are faithful reproductions of the originals.

2. Feature Extraction OCR

Feature Extraction OCR is more advanced than Pattern Recognition OCR. Instead of relying on whole character patterns, this method breaks down characters into their fundamental features—lines, curves, and intersections. 

The software analyzes these features and assembles them into recognizable characters. This approach is beneficial for documents with varied fonts or non-standard text, where pattern recognition might fail.

For example, suppose a law firm needs to digitize a collection of legal contracts using different fonts, sizes, and styles. Some contracts include decorative headings, italicized text, or even handwritten annotations.

Feature Extraction OCR comes into play here, as it dissects each character into its essential components and then reconstructs them based on the identified features. The software can handle the diversity in text presentation, converting even the most stylized fonts into accurate digital text.

This ensures the firm has searchable, editable copies of all its contracts, regardless of how they were originally formatted.

Check out how Valtatech drives 3x faster data extraction from 20k+ invoices monthly

3. Intelligent Character Recognition (ICR)

Intelligent Character Recognition (ICR) is designed to tackle one of the most challenging tasks in OCR: recognizing handwritten text. Unlike traditional OCR, which struggles with the variability and unpredictability of handwriting, ICR uses machine learning algorithms to improve its accuracy over time. 

ICR processes more handwritten documents and learns from its mistakes, becoming more proficient at recognizing different handwriting styles.

For example, a bank processes thousands of handwritten loan applications every month. These applications include crucial customer information like names, addresses, and financial details. This would have required manual data entry in the past, but the bank can automate the process with ICR. 

As the ICR software reads each form, it learns to identify the applicants' unique handwriting styles. Over time, the software becomes increasingly accurate, reducing errors and speeding up the application process. This automation allows the bank to approve loans faster, improving customer satisfaction and operational efficiency.

4. Optical Mark Recognition (OMR)

Optical Mark Recognition (OMR) is a specialized type of OCR designed to detect and interpret marks on a document, such as checkboxes, bubbles, or tick marks. 

Unlike other forms of OCR, OMR does not recognize text. Instead, it identifies the presence or absence of marks in predefined positions. 

OMR is commonly used to process surveys, tests, and standardized forms in which respondents fill in bubbles or checkboxes.

For example, an educational institution administers multiple-choice exams to thousands of students. Each student fills out a bubble sheet, marking their answers by filling in circles. 

After the exam, the institution grades the tests using OMR technology. The software scans each sheet, detects the filled bubbles, and records the answers.

Within minutes, the system processes hundreds of tests, calculates scores, and generates reports. This saves time and eliminates the potential for error in grading, ensuring that students receive their results quickly and accurately.

5. Optical Word Recognition (OWR)

Optical Word Recognition (OWR) focuses on recognizing entire words rather than individual characters. This method is beneficial in languages where words are often combined or context is needed to interpret the text correctly. 

OWR uses dictionaries and linguistic algorithms to understand and convert the text into a digital format, often improving accuracy by considering the context in which the words appear.

For example, a publishing company needs to digitize books written in German, a language known for its long compound words. Using traditional OCR might result in errors, as the software could struggle with the complex structure of these words. 

However, with OWR, the software recognizes entire words rather than breaking them down into individual characters. By referencing a built-in dictionary and analyzing the surrounding context, OWR accurately interprets and digitizes the text, preserving the meaning and structure of the original content. 

It ensures that the digital copies of the books are accurate to the originals, with all compound words correctly identified.

6. Mobile OCR

Mobile OCR is designed for mobile devices, where lighting, camera quality, and movement vary significantly. Its technology is optimized to work in real-time, enabling users to capture and recognize text directly from their smartphones or tablets. It is widely used in apps for scanning business cards, receipts, documents, and barcodes.

For example, a delivery driver uses a mobile app with OCR capabilities to manage package deliveries. The driver scans barcodes, addresses, and delivery notes using the smartphone’s camera throughout the day. 

Mobile OCR technology instantly recognizes text, such as tracking numbers and customer information, and updates the delivery status in real time. Even if the conditions are less than ideal—like poor lighting or a slightly blurred image—the app can still accurately extract the necessary information. 

This streamlines the delivery process and ensures the data is accurately recorded without manual entry.

Benefits of Using OCR for Enterprises

Lending and Insurance enterprises use tons of data for underwriting and claim settlement. Similarly, logistics, healthcare, law, IT, and commercial real estate depend heavily on data. OCR makes it easier to extract data from all these documents and use it for analysis:

7 Applications of OCR (with Industry-specific Use-Cases)

It is quite likely that you have used OCR technology in your life if you have used an app such as CamScanner to take photos of business cards. 

When you upload photos & PDF files to Google Drive, Google automatically scans them using OCR technology to identify text in them. Other applications of OCR are:

• Extracting data from business documents, for example, bank checks, invoice, bank statement and receipts
• Recognizing number plate recognition in traffic cameras & CCTVs
• Extracting data from passports at airports
• Extracting data from business cards
• Key value pair and table extraction from insurance documents
• Making physical books readable online
• Making documents searchable

‍

Here are some real-life industry-specific use-cases of Optical Character Recognition technology:

1. Communication 

Among the most common applications for OCR is the digitization of books and unstructured materials, which facilitates human-to-human communication. Google Translate's OCR technology, which allows users to read in any language, is one such example. 

2. Banking 

Bank statements need a substantial quantity of data input for banking activities. Checks may be deposited digitally and processed within a few days with OCR-based check depositing tools in mobile banking applications. 

Monitoring and evaluating your customers' data, including personal and security information, is another application of OCR in the banking sector.

3. Insurance

Business OCR can also contribute to the rapidly expanding insurance industry. Specifically, OCR can automate the processing of insurance claims for speedier transactions. 

4. Legal

OCR enables legal businesses to scan their printed documents, including affidavits, judgments, files, declarations, and wills, among others. 

5. Healthcare 

The OCR may contribute significantly to the healthcare business. OCR technology may digitize data from X-ray reports, patient histories, treatments or diagnoses, tests, and overall hospital records. 

6. Tourism 

With OCR, travelers and hotel guests may now check in instantly by scanning their passports to a hotel's website or mobile app which directly sends the information to the hotel's operations software, thus making the process faster and smoother.

7. Retail

With mobile OCR, users may now redeem certificates by scanning their mobile phones for serial numbers. Unquestionably, outsourcing data collecting is required to improve the management of actionable data. 

Even more advantageous is the introduction of corporate automation processes, notably machine learning, which can function 24 hours a day, seven days a week, and substantially quicker than human specialists, automatically improving data collection outsourcing procedures. 

As machine learning evolves, it extends beyond data collecting and has major applications in several sectors.

Limitations of OCR

There are 2 main shortcomings of OCR technology: Accuracy and Text categorization. Let's talk about both, and more in detail:

1. Data capture accuracy

One of the issues with OCR technology is that the accuracy may not be 100%. 

For example in the image below "21.08.2018" could be captured as "2I.O8.2OI8". Hence, you need a second system that validates the output of the OCR engine.

2. Text categorization

OCR technology identifies characters and then combines those characters into words. However, for business use, it is important to identify what those words mean. 

For example, OCR technology will give the output “Invoice No: 12345” where “Invoice No” represents the “invoice_number_key” and “12345” represents “invoice_number_value". This is where you need intelligence built on top of base OCR technology to make the identified text usable.

3. Image correction 

When individuals snap photographs of their ID papers with their cell phones or webcams, the images must typically be de-skewered and reoriented if they were not correctly aligned, so that the OCR system can extract the data. 

4. Colored backgrounds 

OCR must frequently transform a color/grayscale picture to black and white to eliminate fuzzy text and better distinguish black and white text from its backdrop. 

5. Glare and blurred images 

What happens if there is glare or the user moves slightly when their ID is being photographed? When there is glare or blurriness in the ID image, the likelihood of data extraction errors increases dramatically. 

Data capture requires more than just OCR 

Multiple processes are necessary to extract and arrange the information when consumers take a photo of their ID with their smartphone or webcam. 

The first step is to properly identify the type of identification present. This enables the engine to appropriately arrange the information read by the OCR, which includes determining the first name, last name, date of birth, and any other relevant field. 

Without additional AI or technology specifically trained to distinguish ID types, OCR alone will lack the precision required to combat fraud and provide a positive user experience. That's where Intelligent Document Processing comes into play:

How Intelligent Document Processing (IDP) differs from OCR? 

As we mentioned earlier, "Data structuring requires more than just OCR" can't be more true. OCR can recognize characters but it can't assign context to the text. In a way, OCR reads characters but it doesn't understand it. 

With the help of Artificial Intelligence and Machine Learning, Intelligent Document Processing (IDP) not only reads text but assigns context in order to provide more accuracy and usable data for analysis. Let's take a look at where IDP fairs compared to OCR:

1. High rate of data processing 

Not only does IDPs rapid processing save time and money, but it also allows team members to focus on other business objectives. It's quite a revolution to be able to extract and categorize data in seconds as opposed to hours, which is why organizations constantly desire fast speed. 

2. Simple to configure and initiate 

The future of technology favors products that adhere to simplicity and the "easy-to-use" phenomena, particularly in the technology sector. Even the most complicated systems seek easier solutions for their clients; this is not rocket science. In light of OCR and Acodis, OCR is considered to be less "easy" to set up and begin than the AI processes. 

3. Capable of totally automating document processing 

While a completely automated data processing system is not on the wish list of every organization, this feature is crucial to your and your company's productivity. This aspect is absent from OCR, necessitating that users regularly submit templates and monitor data processing. This is, in all honesty, a waste of time.

4. IDP Understands your data like a person 

OCR is capable of interpreting simple text, numbers, and symbols, but it lacks Acodis' cutting-edge contextualized knowledge. Using OCR technologies to contextualize and alter important dates on an insurance policy, for example, would not be feasible. Furthermore: 

OCR can identify some pixels as the integers 1 9 8 0; however, it does not comprehend that this is a year and part of your Date of Birth.

IDP does, though. As humans, we rapidly comprehend the meaning of particular words, and IDP does the same for you. It can interpret text and documents. 

5. Auto-teaching system 

When it comes to integrating data automation, the capacity to be efficient is a recurring topic. Unlike regular OCR, IDP is able to learn and evolve without the need for ongoing assistance. 

6. Quickly and easily compatible with other current systems 

Unlike OCR, IDP makes it possible to install the program into your organization with minimal to no complications or concerns. This function can save both time and money. Being readily integrated relieves you and your team of responsibility if you lack technological expertise. 

Some Key Functions of OCR Software

Listed are some key functions for OCR Software:

• Text Recognition: The software identifies and extracts printed or handwritten text from images or scanned documents.
• Data Conversion: Once the text is recognized, the software converts it into an editable format such as a Word document, Excel sheet, or searchable PDF.
• Automation: OCR software automates data entry, reducing the need for manual transcription and minimizing errors.

6 Best OCR Software in 2024

OCR (Optical Character Recognition) software is a tool for converting different types of documents, such as scanned paper documents, PDFs, or images captured by a camera, into editable and searchable text. 

The primary function of OCR software is to "read" text from images and convert it into a digital format that can be manipulated on a computer or other digital devices.

1. Docsumo

Docsumo is a powerful AI-driven OCR software designed to automate data capture, extraction, and processing across various document types. Leveraging intelligent OCR, AI, and machine learning algorithms, Docsumo excels in digitizing documents and converting them into multiple formats with high accuracy and reliability. 

It supports many document types, including handwritten forms and complex layouts, making it a versatile solution for businesses needing efficient document management. With seamless integration capabilities, security features, and a user-friendly interface, Docsumo is ideal for organizations looking to streamline operations, reduce costs, and enhance customer service. 

Continuous updates and extensive training resources ensure that Docsumo remains at the forefront of OCR technology.

Key Features

• Intelligent (AI-powered) OCR: Advanced AI-driven OCR technology ensures accurate text recognition across diverse document types.
• Document Data Extraction: Efficiently extracts relevant data from various documents, including those with complex layouts.
• Batch Processing: Handles large volumes of documents simultaneously, optimizing workflow efficiency.
• Document Fraud Detection in Real Time: Identifies fraudulent documents instantly, enhancing security and reliability.

Pros

• Customization and Automation: Offers extensive customization options, automating document processing tasks to meet specific business needs.
• Seamless Integration: Integrates smoothly with existing business systems, providing robust APIs for easy data exchange and workflow automation.
• Customer Support: Provides excellent customer support, ensuring users receive the assistance they need for optimal utilization.

Cons

• API Documentation: The API documentation could be more detailed to support developers better.
• Growth Phase: Docsumo is still in its growth phase, so that some features may be under development or refinement.

Pricing

Docsumo offers tailored pricing based on the organization's specific needs and scale. Interested users can contact Docsumo for detailed pricing information and customized plans.

2. Tesseract OCR

Tesseract OCR is a robust open-source tool widely used by developers and researchers for text recognition. Known for its high accuracy and versatility, Tesseract can be integrated into custom applications or automation workflows, mainly through installation as a Python package using PIP. 

Its open-source nature and advanced features make it a go-to solution for those who need a reliable OCR engine without commercial constraints.

Key Features

• Advanced OCR Engine: Powered by LSTM networks, Tesseract delivers high accuracy in text recognition, making it suitable for a wide range of applications.
• Multi-language Support: Tesseract can recognize over 100 languages, providing extensive language coverage for global use cases.
• Customization: The OCR engine can be trained on custom datasets, allowing for specialized text recognition tasks.

Pros

• Free and Open-Source: As an open-source project, Tesseract is freely available, providing powerful OCR capabilities without licensing costs.
• High Accuracy: Tesseract offers impressive accuracy, especially for printed text, thanks to its use of LSTM networks.
• Extensive Language Support: Tesseract supports over 100 languages, Making it ideal for multilingual projects or international applications.

Cons

• Outdated Compared to Commercial Options: While Tesseract is highly capable, it may not match the latest AI-driven commercial OCR engines in terms of features and adaptability.
• No Graphical User Interface (GUI): Tesseract needs a user-friendly GUI, which can make setup and usage challenging, especially for non-technical users.
• Complex Setup: Implementing Tesseract requires technical expertise, particularly in environments where custom training or integration is needed. This limits its accessibility to developers and tech-savvy users.

Pricing

Tesseract OCR is entirely free to use, making it an excellent choice for developers, researchers, and tech enthusiasts who require a powerful OCR tool without the financial commitment of commercial software.

3. Adobe Acrobat OCR

Adobe Acrobat OCR is a premier PDF management solution that offers a broad range of tools for creating, editing, signing, and sharing PDF documents. Integrated with Adobe Document Cloud, it ensures easy file access and supports seamless document management across multiple devices. 

Known for its advanced features, including OCR for text recognition and robust file conversion capabilities, Adobe Acrobat DC is an all-encompassing solution ideal for individual users and teams.

With optical character recognition (OCR) in Adobe Acrobat, you can instantly extract text and convert scanned documents into editable, searchable PDF files.

Key Features

• OCR Technology: Transforms scanned documents and images into fully searchable and editable text, maintaining the original layout and formatting.
• Editing and Commenting: This feature allows users to edit PDFs swiftly, add annotations, and compare different versions of documents side by side.
• File Conversion: Facilitates the conversion between various file formats and PDFs, ensuring that documents are accessible and easy to share.
• Cloud Integration: Offers seamless access and management of documents through integration with Adobe Document Cloud, allowing file access from any device.

Pros

• Comprehensive PDF Management: Adobe Acrobat DC is a versatile tool that excels in all aspects of PDF handling, from creation to collaboration, making it a reliable choice for professionals and businesses.
• Search and Edit Features: The ability to search text within PDFs and make immediate edits significantly enhances productivity, particularly for tasks that require quick revisions or updates.
• Collaborative Capabilities: The software’s advanced collaboration features enable multiple users to work on the same document simultaneously, providing real-time feedback and comments, which is invaluable for team projects.

Cons

• Steep Learning Curve: Due to its extensive features, new users may find the interface complex and challenging to navigate initially, requiring time to master.
• High Cost: Adobe Acrobat DC is priced higher than many other PDF management tools with OCR capabilities, which might be a limiting factor for individuals or small businesses operating on a tight budget.

Pricing

• Adobe Acrobat Standard: $12.99 per month (billed annually)
• Adobe Acrobat Pro DC: $19.99 per month (billed annually)
• Adobe Acrobat Pro DC for Teams: Pricing is available upon request and tailored to businesses and teams requiring multiple licenses and enhanced collaboration tools.

4. Amazon Textract

Amazon Textract is a fully managed machine learning service that automatically extracts text and data from scanned documents, forms, and tables. Textract can accurately identify and extract information from complex backgrounds using advanced computer vision technology. 

Its unique Selective Context Attentional Scene Text Recognizer (SCATTER) feature enhances its ability to handle a wide range of text, including handwritten notes and various currency symbols, making it a versatile tool for businesses looking to automate document processing.

Key Features

• Form and Table Extraction: Automatically identifies and extracts data from forms and tables while preserving their structure and relationships.
• Handwriting Recognition: Accurately reads and digitizes handwritten text, making it suitable for processing handwritten forms and notes.
• Bound Boxes: Uses bounding boxes to capture and extract text from specific areas within documents precisely.
• Workflow Management: Seamlessly integrates into various workflows, automating complex document processing tasks.

Pros

• Process Automation: Integrates OCR with AI to enhance automation, reducing the need for manual data entry and improving operational efficiency.
• High Flexibility: A flexible billing model allows businesses to scale their usage based on specific needs.
• Cloud-Based Solution: Textract integrates easily with other AWS services, providing a scalable and accessible solution for businesses.

Cons

• Inconsistent Accuracy: While Textract is powerful, its accuracy can vary, sometimes requiring additional refinement to achieve the desired results.
• Performance Variability: The tool's performance may differ across various data types, necessitating adjustments to maintain optimal output.

Pricing

Amazon Textract is available through the AWS Free Tier, allowing users to start using the service at no cost. Pricing beyond the free tier depends on the volume of documents processed, making it a flexible option for businesses of all sizes.

5. Google Doc AI

Google Document AI is a cutting-edge, fully managed machine learning service designed to automate document processing and extract valuable data from structured and unstructured documents. Utilizing generative AI, Google Document AI provides developers with high-accuracy processors to classify, split, and extract data from various document types.

The platform seamlessly integrates with Google Cloud products like BigQuery and Vertex AI, making it an ideal solution for enterprises looking to enhance data-driven decision-making and streamline operations.

Key Features

• Generative AI for Document Processing: Leverages the power of generative AI to extract and classify data with high accuracy, requiring minimal training.
• Document AI Workbench: Provides tools to build custom processors that can classify, split, and extract data from documents, allowing for easy customization and fine-tuning.
• Enterprise OCR: Advanced OCR technology supports over 200 languages and includes features like handwriting recognition and the ability to detect complex document layouts.
• Form Parser: Extracts fields and values from standard forms and tables, capturing data without customization.

Pros

• Faster Time to Value: Generative AI enables quick data extraction and document classification without extensive training, accelerating the time to insights.
• High Accuracy: The platform’s advanced models and fine-tuning capabilities ensure that even complex documents are processed accurately.
• Seamless Integration: Google Document AI integrates effortlessly with other Google Cloud products, providing a unified data processing and analytics environment.

Cons

• Accuracy Variability: Although consequential, Google Document AI's accuracy may vary depending on the complexity and diversity of the documents, requiring further refinement in some cases.
• Learning Curve: While competent, the platform’s advanced features and customization options may present a learning curve for new users or smaller teams.

Pricing

Google Document AI offers a transparent and flexible pricing model, with a $300 free credit for new customers to explore its capabilities. 

The cost varies depending on the volume of documents processed and the specific features used, making it suitable for both small and large-scale applications. 

Detailed pricing information is available on the Google Cloud pricing page.

6. Microsoft Lens

Microsoft Lens, formerly known as Office Lens, is a versatile app designed to capture and digitize various types of content, from documents and whiteboards to business cards and handwritten notes. By removing shadows and adjusting angles, Microsoft Lens ensures your images are clear and easy to read. 

This app integrates seamlessly with Microsoft Office products, allowing users to upload captured images directly to OneNote, Word, PowerPoint, or OneDrive and save them as PDFs.

Key Features

• OCR Technology: Converts scanned images into editable and searchable text, making it easy to digitize documents and notes.
• Multi-Mode Capture: This feature offers multiple capture modes, such as Whiteboard, Document, Business Card, and Photo, to optimize image quality for different types of content.
• Cloud Integration: Seamlessly uploads captured images to Microsoft OneNote, OneDrive, Word, and PowerPoint for easy access and sharing across devices.
• Handwriting and QR Code Recognition: Recognizes handwritten text for digitization and can scan QR codes for quick access to information.

Pros

• Versatile and User-Friendly: Microsoft Lens is a powerful tool for digitizing various types of content, from handwritten notes to business cards, making it highly useful for personal and professional use.
• Enhanced Image Quality: The app automatically adjusts images to remove shadows and correct angles, ensuring the captured content is clear and legible.
• Seamless Integration with Microsoft Office: Easily integrates with OneNote, Word, PowerPoint, and OneDrive, providing a smooth workflow for managing and sharing documents.

Cons

• Limited Features for GCC Accounts: Certain advanced features, like Business Card scanning and image-to-text conversion, need to be supported for GCC accounts, which could be a limitation for some users.
• No Standalone GUI: While it offers robust features, Microsoft Lens does not have a desktop interface, which may limit its use to mobile devices only.

Pricing

Microsoft Lens is free on the Google Play Store, making it accessible to users looking to enhance their document management capabilities without incurring additional costs.

How to Choose the Best OCR Software?

Choosing the fitting Optical Character Recognition (OCR) software is crucial for maximizing efficiency, accuracy, and return on investment. With numerous options available, it’s essential to consider several key factors to select the software that best fits your needs. 

Here are the three essential criteria to consider when choosing OCR software. Highlight Docsumo, a leading OCR solution known for its advanced capabilities.

1. Accuracy and Language Support

Accuracy is the most critical factor in OCR software, as it directly impacts the quality of the text recognition. Look for OCR solutions with high accuracy rates, especially for the documents you need to process. If you deal with multi-language documents, choosing software that supports multiple languages and can handle various scripts and fonts is essential.

Why it Matters?

Imagine processing legal documents or financial documents with complex data and intricate language. Even a tiny error in recognizing numbers or legal terms can have significant consequences. Therefore, accuracy is critical.

2. Integration and Compatibility

The OCR software should seamlessly integrate with your existing systems, whether a document management system, a CRM, or an ERP platform. Compatibility with various file formats, such as PDFs, images, and Microsoft Office files, is also essential. The easier it is to integrate OCR software into your workflow, the more efficiently you can digitize and manage your documents.

Why it Matters?

If your organization already uses tools like SAP, Salesforce, or Microsoft Dynamics, choosing OCR software that integrates smoothly with these systems will streamline your processes, saving time and effort.

3. Scalability and Customization

Your OCR software should be able to scale to meet your business needs. Whether you’re processing a few documents daily or thousands, the software should handle increasing volumes without compromising performance. 

Additionally, customization options, such as configuring the software to recognize specific types of documents or fields, can significantly enhance its utility for specialized tasks.

Why it Matters?

A growing business needs OCR software that can keep up with increased demand. For example, if you plan to expand your operations or add new services, your OCR solution should be flexible enough to adapt to these changes without requiring a complete overhaul.

Conclusion: Why Choose Docsumo’s OCR Software?

When selecting OCR software, Docsumo is an industry leader that excels in these critical areas. Docsumo’s OCR solution offers unmatched accuracy, especially for processing complex documents like invoices, receipts, and forms. 

It supports various languages and document formats, making it versatile for businesses across different industries.

One of Docsumo's standout features is its ability to integrate seamlessly with popular business tools. This ensures that the data you extract can be automatically synced with your existing systems. 

Additionally, Docsumo offers robust scalability, allowing businesses to handle everything from small batches of documents to large-scale processing needs. 

The software is also highly customizable, enabling you to tailor it to your specific use cases in finance, healthcare, logistics, or another industry.

Benefits of Docsumo

• 93%+ Touchless Processing: Automates the extraction and processing of data with minimal human intervention.
• Seamless Integration: Easily connects with your existing systems for a smooth workflow.
• Scalable Solutions: Handles both small and large document volumes efficiently.
• Customizable Workflows: Adapt the software to suit your unique business needs.

To experience the benefits of Docsumo, you can start a 14-day free trial now.

Selecting the right OCR software can transform your business operations, reducing manual effort, increasing accuracy, and improving overall efficiency. 

Focusing on factors such as accuracy, integration, scalability, and customization can ensure that the OCR solution you choose meets your current needs while being flexible enough to adapt to future challenges. 

With its advanced features and user-friendly design, Docsumo offers a powerful OCR solution that ticks all these boxes, making it an excellent choice for businesses looking to harness the full potential of OCR technology.