OCR stands for Optical Character Recognition and it is the identification of text from images and documents. This in-depth guide about OCR will help you better understand the technology, its benefits and the best OCR software available on the market.
What does OCR means?
A human and a computer do not interpret text and digital images in the same way. As humans, we just see alphabets and letters but computers see them in binary format (0s and 1s).
An OCR technology will convert those binary numbers into machine-readable format (Text, JSON, HTML). In this way, OCR can be used to extract text from images and PDF files automatically. The modern OCR technologies make use of machine learning and artificial intelligence (AI) to better understand the handwritten text and different languages as well.
As per a report by Market Analysis Report published in February 2021, the global optical character recognition market size is expected to increase at a CAGR of 16.7%.
History of OCR
The history of OCR dates back to World War I when physicist, Emanuel Goldberg invented the machine that could read and convert characters in telegraph code. It was called the “Statistical machine” which was later acquired by IBM.
In the 1970s, Ray Kurzweil developed the first Omni-font OCR which could recognize any type of font. And, as from the year 2000, OCR tools became easily available and accessible on the cloud, desktop, and mobile-based applications. Nowadays, OCR can even recognise handwritten text quite reliably such as reading addresses on physical emails or cheques.
Where can OCR be used?
OCR is widely used to read and extract data from invoices, medical records, bank statements, and receipts.
How does OCR work?
There are 3 stages in the OCR process namely:
- Image pre-processing
- Character recognition
Before the OCR software can do its magic, it’s important to ensure that the accuracy and quality of the scanned documents or image files are up to the level. This is where image pre-processing comes into play. Several techniques are used in this first step such as:
- Noise removal
- Skew correction
All those methods help to improve the quality of the input.
This process uses AI to identify and analyze the characters within an image. There are two methods that can be used:
- Pattern recognition where a range of text formats are used to train the AI. It can then compare the letters and matches them correctly.
- Feature recognition is rule-based and is focused on specific characteristics; for example, curved lines.
The last step involves improving the accuracy of the data by correcting the errors. When training the Machine Learning algorithm, the AI is taught about what the final outcome should look like. Thus, the program can compare and verify whether everything is within the standard vocabulary and language data and correct accordingly.
OCR is heavily dependent on the underlying alphabet (Latin or Arabic or Chinese for example). But the best OCR platforms are also heavily trained on specific languages in order to provide the best accurate results. Nowadays the best OCR is done for documents in English, but other languages are catching up very fast.
What are the best and free OCR software?
There are different types of OCR software today; some of which are specific to a language or a business domain. In the below list, you'll find the OCR tools which are mostly used nowadays.
1. Tesseract OCR
Tesseract is a free and open-source OCR software released under the Apache 2.0 license. The program can easily identify more than 100 languages and is perfect for OCR scanning. Tesseract OCR supports deep learning data models as well.
In 2006, Google sponsored Tesseract and it was considered as the most accurate OCR application
Tesseract is available on Windows, Linux and Mac OS. The latest version 5 was released last year and can be installed from here.
2. Amazon Textract
AWS Textract can automatically extract text from scanned documents using AI, machine learning and OCR. Amazon Augmented AI can also be added to Textract to verify sensitive data and implement human reviews of handwritten documents. Amazon Textract has the following features:
- Table and form extraction
- Handwritten recognition
- Identity documents
- Bounding boxes
Amazon also offers an AWS free tier for all new customers which lasts for 3 months.
Reinsurance Group of America, a Fortune 500 company launched an optimization solution in collaboration with AWS Textract to innovate their underwriting process using OCR and machine learning. - Yahoo Finance, January 2022
3. Google Document AI
In 2020, Google announced their new DocAI (Document AI) platform for document processing automation. It is built on AI and machine learning enabling effortless data extraction workflows.
You can test how Document AI works using one of their samples. The software has natural language processing (NLP) capabilities as well to process high volume of scanned paper documents as well.
OCR is only the first step of an end-to-end document processing workflow. Most businesses not only want to be able to read the text from their incoming documents using OCR, but they also need to extract specific data points that can fed into their systems. For example, an accounting department needs to extract text from their supplier invoices but also automatically recognise the supplier contact details, invoice amount and individual line items. This is where more advanced solutions like Parseur can help.
Parseur is a powerful no-code document processing tool which uses OCR to automate data extraction from PDFs. It is quick and easy to set up and use for any type of industry. All you have to do is forward your documents to the Parseur mailbox and watch the robots do their witchcrafts.
There are no parsing rules and documents are processed within seconds. You can customize the data fields and create as many templates as you want. Parseur uses machine learning to pick up the right template for your documents.
Depending on the type of document or PDF, Parseur can automatically extract data with its built-in library of templates. It has the ability to extract data from tables as well and can connect to different applications such as Zapier, Integromat and Power Automate.
What are the main use cases for OCR?
Parseur is used by hundreds of company around the world to extract data from PDFs. Most common OCR use cases we are seeing are:
The future of OCR
Of no doubt, an intelligent OCR software can make a difference in the way organization processes their documents. With the rise of new technologies and capabilities such as deep machine learning and AI, OCR systems will continue to dominate the global market.
The OCR market will be worth around $26.31 Billion By 2028.
—Press release by Market Insights , February 2021
If companies want to lead with digital transformation, this form of document data capture will need to be part of their workflows.