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Legal informatics - the digitization of legal information – is an emerging trend all over the world. The first step of legal informatics is the extraction of text from legal documents using digital tools. Optical Character Recognition (OCR) software is useful for text extraction from legal documents, and when driven by AI technology, it can become a powerful and essential part of legal informatics.



Legal OCR extracts text from scanned documents and images and converts them into meaningful digital data for subsequent use or archiving purposes. The conversion of legal documents and data into the digital format can help in easy retrieval of data, which can in turn help in faster and better analytics and informed decision-making in all aspects of the legal machinery.

Legal establishments are always pictured in the background of neat rows of hard-bound law books on shelves and packed file cabinets that bear reams of case sheets. There are many kinds of legal documents such as contracts, law commission reports, tribunals, case sheets, acts, agreements, etc., used in various settings and scenarios. A legal document is a repository of valuable information in legalese, largely unintelligible to the untrained. Even to the trained law professional, the amount of data contained in a legal document can be unwieldy with important data surrounded by supporting verbiage.

Lawyers and judges must frequently refer to specific acts, sections, articles, rules or orders of an act that are relevant to the case being handled. For this, keywords from the case description must be identified from many pages of case sheets. The legal department of large companies must keep track of contracts, negotiations, takeovers, bids and other such legally binding documents.

The high document volume is complicated further by inconsistent filing practices, which makes access to information exceedingly difficult and time-consuming even for the most competent law professional.

Courts, the judiciary, and the public seeking justice, all over the world, face problems of delays and runaway expenses. While there are many causes for these problems, the lack of operational efficiency and coordination is pervasive and arises from the manual nature of document and data management.

Digital extraction of text is the first important step toward efficient digital management of legal documents and information, which can, in turn, enable convenient and quick information retrieval at any time. In the simplest example, a quick keyword search in a database by a law professional can provide her with sufficient information to choose and act, section, article etc. relevant to the case being handled. The legal department of companies can benefit from digital text management of legal data in that they can retrieve information on contracts, agreements, legislation and policies without having to disappear into the dusty basement of paper archives for hours or even days.


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The growth in the use of digital tools and automation in the legal departments of companies in recent times has been rapid.

According to Tracxn’s Legal Tech Sector Landscape Report, US law firms invested $2.7 billion in automation in the legal sector offices in the past two years alone.

Gartner reports that by 2025, large organizations will require at least four legal technology vendors for the management of their legal aspects and will prioritize legal tech and legal automation to manage the increasingly complex demands of the judicial system. Naturally enough, the proportion of legal budgets spent on technology is expected to increase drastically by 2025.

Data from Gartner.

While the use of digital tools to extract and manage legal documents is indeed inroads in the business sector, the computer has not yet found a consistent place alongside the balance scale and the judge’s gavel in the courtroom as yet. One reason for the slow insurgence of IT and digital tools into the judiciary has been a lack of awareness of the tools available and their benefits to the daily functioning of the judicial machinery.

Any kind of digitization endeavour starts with the extraction of text from paper documents in a meaningful way. The automated extraction of text from legal documents is different from simple data extraction from other kinds of documents because of their length, their complex structure, and unique vocabulary.

One of the primary aims of data extraction is to enable the retrieval of data at will using a specialized search engine or other digital tools. Common areas that need to be retrieved in various aspects of the legal business are shown below, and the text extraction tool must be able to categorize data into broad fields that mirror these (or similar) search parameters.


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There are many types of OCR software that are used in data extraction in the industry today. The most rudimentary type simply extracts all the text from the document and further categorization and meaningful data extraction needs human effort. This is unsuitable for legal text extraction because it can become labour intensive to categorize and sort the extracted raw data.

Rudimentary OCR text extraction from a legal document – all readable text is picked out with no discrimination or characterization

The second generation of OCR – Zonal or Template-based OCR – extracts specific data from the delivery document, depending on its position or “zone” in the document. This is better suited for legal text extraction because it can be programmed to extract specific sections of the legal document. However, this is also not perfectly suited because not all documents are of the same format, and the nature of documents also varies considerably, which does not match the one-shoe-fits-all style of text extraction of zonal OCR.

Zonal OCR text extraction from a legal document – marginally better than rudimentary OCR but not best suited

This is where Intelligent OCR tools are useful. Intelligent OCRs can be of three types:

  • Robotic process automation (RPA) mimics human actions in repetitive tasks.
  • Artificial intelligence (AI), computer science's "Holy Grail" in the words of Bill Gates, mimics human judgement and behaviour to match case numbers and files.
  • Machine learning (ML) is a subset of AI in which the computer “learns from experience” through algorithms such as the Neural Network that mimics the learning process of the brain.

The AI and ML tools to document understanding use statistical methods, neural networks, decision trees, and rule learning techniques. A full end-to-end AI system for document understanding typically employs the following tools:

  • Computer-vision-based document layout analysis tool: This partitions the document page into regions with distinct content to differentiate between relevant and irrelevant regions and categorize the type of content recognized. The zonal OCR tool could also be used to locate and transcribe the selected text.
  • Information extraction tool: This uses the OCR output or document layout to recognize the information embodied in the data extracted. The AI and ML algorithms look for specific types of information, such as reference numbers, names, addresses, costs, etc.

Some commonly used AI-based OCR tools are Nanonets OCR API, Tesseract, Ocular, SwiftOCR, and Calamari. The Nanonets OCR API uses state-of-art AI algorithms that allow the design of custom OCR models. Data can be uploaded, annotated, and the model can be trained easily and seamlessly integrated with existing systems. For training and learning of the AI models, a certain amount of human validation would be required to test a small sample of the model’s performance to check for accuracy or incorporate course correction to the algorithms for more accurate data understanding.

In most AI-based document understanding software, the text extraction component is integrated with quality checks and data preparation software to clean and organize data after scraping. It also incorporates data integration tools to combine multiple data types and sources and aggregate them in one place. Good AI-data extraction tools can extract structured, poorly structured, and unstructured data, pull data from multiple sources, and export extracted data in multiple readable formats.

All of the above factors make AI-enabled OCR best suited for the extraction of data from legal documents


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Advanced automated data processing can capture pertinent data from all kinds of legal documents, based on training, and can auto-process them in a way that mimics the human mind. AI-enabled OCR tools are best suited for text extraction from legal documents due to the following reasons:

Structured Data Extraction

AI-enabled Legal OCRs can extract text from documents that may be variably structured, poorly structured and/or unstructured.

Data Classification

Legal OCRs can categorize data into desired categories depending on the level of training and instructions provided by the end-user.

Data Standardization

The extracted data into multiple readable/editable formats for subsequent use.

Data security

Legal information is highly sensitive and confidential.  The American Bar Association reported in 2020 that just over one in five law firms did not know if their firm had experienced a security breach. The text extraction software must be able to ensure safeguarding the data from theft, hacking, and mismanagement.  The possibility of introducing checks at various levels of the automation process initiated by AI-enabled Legal OCR can enhance data security.

Accuracy of data

Legal OCRs that leverage AI can minimize or even completely eliminate human errors caused by fatigue or oversight.

Time savings

Manual data entry from legal documents can be time-consuming, and Legal OCRs can save much of the time spent by employees in mundane repetitive activities. AI-enabled OCR extracts relevant data from any document in 27 seconds as against 3.5 minutes for manual capture.

Task reorientation

The time available to the legal expert due to automation of text extraction can be rerouted to actual legal tasks of consequence.

Centralized data

The text captured by the Legal OCR software can be stored in a centralized location in a logical manner that can be accessed easily by multiple participants in the judicial process.


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Nanonets is an OCR software that leverages AI & ML capabilities to automatically extract unstructured/structured data from PDF documents, images, and scanned files. Unlike traditional OCR solutions, Nanonets do not require separate rules and templates for each new document type.

Relying on AI-driven cognitive intelligence, Nanonets can handle semi-structured and even unseen document types while improving over time. The Nanonets algorithm & OCR models learn continuously. They can be trained or retrained multiple times and are customizable. You can also customize the output, to only extract specific tables or data entries of your interest.

While offering a great API & documentation for developers, the software is also ideal for legal teams that have no prior knowledge or expertise with technology.

The benefits of using Nanonets over other automated OCR software go far beyond cost savings, accuracy, and scale. Nanonets additionally provide unique benefits that place them far ahead of the competition:

  • A truly no-code tool
  • No post-processing is needed
  • Works with custom data
  • Easily handles data constraints
  • Works with multiple languages
  • Continuous learning
  • Infinite customization

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Conclusion

OCR can be used in the legal domain in many ways. Many scanners now have built-in OCRs that do the basic text extraction, i.e., they convert the image into editable text, which can then be post-processed by a human in the loop. Stand-alone, third party OCR software such as Nanonets can provide better text extraction capabilities due to the AI systems built into it. When integrated into a larger document management system, OCR tools like Nanonets can enable not only methodical archiving of data but also simple searchability, which can save the legal professional considerable time and effort.


Nanonets online OCR & OCR API have many interesting use cases that could optimize your business performance, save costs and boost growth. Find out how Nanonets' use cases can apply to your product.


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