What is Optical Character Recognition (OCR)?

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OCR stands for optical character recognition, a technology used to convert scanned images of text into machine-encoded text. When humans read text, they look at an image of a word and comprehend it because their brain has been trained to interpret the sequence of characters as a meaningful word. Optical character recognition mimics this process by taking in a two dimensional image, putting it through multiple states of compression, and extracting the information that’s of interest to the end user. For example, when a paper is uploaded into a computer via a regular scanner, the document is uploaded as a JPEG that cannot be edited in a word processor. An OCR scans each character on the page individually, recognizes them as letters and punctuation marks, and uploads them as text documents instead of JPEGs.

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Optical character recognition is critical because it lets you interact with the content of the document. With OCR, you’re able to search for content, highlight and annotate, tag information, leave comments, edit, and even build machine learning algorithms that extract data. Optical character recognition makes the data in a PDF or an image format actionable and accessible.

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Optical character recognition enables computers to recognize and process written text on images and documents.

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Whether your documents are coming in as physical pieces of paper that need to be scanned or non-searchable PDFs, optical character recognition allows you to digitize that information. Once the information is extracted from the document, it can be passed along to other systems for process management.

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When AI and machine learning are integrated into the process, organizations are able to further minimize human intervention and recognize more varieties of document types and languages to replicate the way the human brain recognizes patterns and context.

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How Does Optical Character Recognition Work?

When you scan a document, how exactly does the software know what it’s looking at?

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Step 1 - Cut out artifacts so your OCR program can concentrate on the text and nothing else. It attempts to remove dust and other various graphics, aligns the text properly, and converts any colors or shades of gray in the image to only black and white. This makes the words themselves easier to recognize.

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Step 2 - Figure out which characters are on the page. Simpler forms of OCR compare each scanned letter pixel by pixel to a known database of fonts and decides on the closest match. Smarter OCR takes this step further by analyzing each character down to constituent elements like curves and corners, and looking for matching physical features and actually letters.

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Step 3 - Make use of a dictionary so it won’t accidentally spit out nonsense words due to to inaccurate scanning. For example, if your scanner sees a word but can’t tell if the middle letter is an O or an A, it can check its own dictionary to decide what the word is. 

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Step 4 - Giving OCR software situational information can further cut down on errors such as telling it to only try to match numbers if it’s reading zip codes on an envelope.

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Even with these tricks, OCR is not perfect, but with greater processing power and machine learning techniques that allow software to recognize more subtle patterns over time, OCR has become versatile enough to recognize harder to read typefaces, inconsistently printed material, and handwriting.

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OCR can be achieved using different approaches ranging from classic computer vision techniques that identify the contour of a character and perform image classification trained on millions of labeled characters to identify its machine readable format all the way to specialized deep learning techniques.

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About Evisort’s OCR

At Evisort, we use best-in-class OCR that’s deep-learning based and has been trained on millions of documents to achieve the highest word level accuracy in the market. It works on over a hundred different languages and has support for handwriting as well.

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Evisort has built proprietary enhancements on top of the OCR to improve spelling quality and perform object detection for things like signatures or key value pairs in a table to optimize the accuracy downstream of the data extraction. Evisort also allows you to preview and interact with documents in its original formatting with an OCR layer on top that lets you search, comment, edit, tag information, and much more.

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What type of documents need Optical Character Recognition

Optical Character Recognition (OCR) is commonly used on the following types of documents:

1. Printed documents: OCR can recognize and digitize text from scanned images of printed pages, such as books, newspapers, magazines, etc. 
2. Handwritten documents: OCR can recognize and digitize text from handwritten notes, forms, and letters. 
3. Historical documents: OCR can digitize and preserve historical texts and documents, making them more accessible to researchers and the public. 
4. Legal documents: OCR is used to digitize and process legal documents such as contracts, deeds, and court records. 
5. ID cards: OCR is used to extract information from ID cards, such as passports, driving licenses for verification and record-keeping purposes. 

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OCR can be applied to any type of document that contains text, making it easier to store, access, and process information in a digital format.

Who uses OCR on Legal documents?

OCR technology is often used by various organizations and individuals to process and manage legal documents. Some groups that use OCR on legal documents are:

1. Law firms: OCR technology helps law firms digitize and manage large volumes of legal documents, such as contracts, deeds, and court records. 
2. Government agencies: OCR is used by government agencies, such as courts and departments of justice, to digitize and manage legal documents, improve accessibility and efficiency, and reduce the risk of errors. 
3. Corporations: Companies and corporations use OCR to digitize and manage legal documents for record-keeping, compliance, and dispute resolution purposes. 
4. Legal service providers: Legal service providers, such as e-discovery companies, use OCR to process and manage large volumes of legal documents for their clients. 
5. Researchers and academics: OCR technology is used by researchers and academics to digitize and access historical legal documents, such as old court records and legal texts, for research purposes.

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OCR technology is used by a wide range of organizations and individuals in the legal industry to process, manage, and access legal documents in a more efficient and effective manner.

Are there special groups in Corporations that manage OCR documents for legal and procurement?

Yes, in corporations, there are specific departments or groups that manage OCR documents for legal and procurement purposes. Some of these groups include:

1. Legal department: The legal department is responsible for managing and processing legal documents, such as contracts, deeds, and court records. OCR technology can help streamline this process and make it more efficient.
2. Procurement department: The procurement department is responsible for managing the procurement process, including the purchase of goods and services. OCR technology can be used to digitize and process procurement-related documents, such as purchase orders, invoices, and supplier agreements.
3. Records management department: The records management department is responsible for managing and storing company records, including legal and procurement documents. OCR technology can help automate the process of digitizing and organizing these records.
4. Information technology (IT) department: The IT department is often involved in the implementation and maintenance of OCR systems and software, and may also be responsible for managing the digital storage of OCR-processed documents.

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Different departments within a corporation may have different responsibilities for managing OCR documents for legal and procurement purposes, but all play a role in ensuring the efficient and effective use of OCR technology in the organization.

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