What is OCR (Optical Character Recognition): Overview, and More

How Does Optical Character Recognition Work?

An OCR system is made up of both hardware and software. The service aims to analyze a physical document's content and convert the elements into a script that can subsequently be utilized for processing data. 

For example, consider postal and mail sorting services. OCR is critical to their capacity to rapidly process source and return addresses so that correspondence may be sorted more efficiently. The following three are essential core techniques of the program:

1. Image Pre-Processing

• In the first stage, the technology converts the document's physical shape into a picture, such as a record picture. The purpose of this stage is for the machine's representation to be precise while also removing any undesired aberrations. 
• The concept is subsequently transformed to a black and white rendition, evaluated for bright vs. dark regions (characters). 
• The image is then segmented into individual pieces, such as spreadsheets, text, or inset graphics, using an OCR system.

2. AI Character Recognition

Artificial Intelligence analyzes the image's dark portions to recognize characters and numerals. Typically, AI uses one of the following approaches to target one letter, phrase, or paragraph at a time:

• Pattern Recognition: Technologies use a range of language, text formats, and handwriting to train the AI system. The program compares the letters on the detected letter picture to the notes it has already learned to find matches.
• Feature Recognition: The algorithm uses rules based on specific character properties to recognize new characters. The amount of angled, crossing, or curved lines in a letter is one example of a feature. 

To identify original characters, the algorithm employs rules based on particular character attributes. For example, one trait is the number of angled, crossed, or curving lines in a character.

3. Post-Processing

AI corrects flaws in the final file during Post-Processing. One approach is to teach the AI a glossary of terms that will appear in the paper. Then, limit the AI's output to those words/formats to verify that no interpretations are beyond the vocabulary.

What Technology Lies Behind OCR?

Optical Character Recognition, or OCR, is a technique that allows you to transform many kinds of documents into customizable and accessible data, such as digitized paper documents, PDFs, or photos acquired by a camera phone. 

A scanner can generate a raster picture that is nothing more than a black and white collection or color dots representing the document. You'll need OCR software to extract and reuse data from document images, camera photographs, or image-only PDFs. This program will single out letters on the image, convert them to words, and then words into phrases, allowing you to retrieve and alter the original letter's information.

OCR Apps/Software

• PDF Scanner: Document Scan+ OCR 

'PDF Scanner:Document Scan+ OCR' is one of the most famous OCR tools, and it tends to garner positive feedback for its user-friendly features. The program, compatible with Android users, allows you to add your signature to papers by importing photos and Pdfs.

• Online OCR

This OCR is likewise very basic and straightforward to use and may be accessed online. In addition, the 'Free Online OCR' is beneficial because it supports 46 languages, including Italian, Portuguese, Spanish, Japanese, and Chinese.

• Office Lens

Office Lens is a mobile-based OCR that Microsoft developed. Its primary function is to convert notes written on whiteboards to digital format. It can also edit digital versions of printed papers, letterheads, and billboards. Its appeal originates from its capacity to improve and optimize photos taken, dynamically resizing them to scale. 

Benefits of Optical Character Recognition

The key benefits of OCR technology are time savings, reduced mistakes, and reduced effort. Compressing into ZIP files, emphasizing phrases, integrating into a webpage, and forwarding to an email are options that aren't available with hard copies. 

While photographing papers allows them to be digitally stored, OCR adds the ability to alter and search those documents.

Applications of OCR

OCR has a wide range of uses, and any company that deals with physical documents can profit from it. Here are a few examples of notable use cases:

• Word Processing

Word processing is perhaps one of the first and most popular applications of OCR. Print files may be scanned and turned into modifiable and accessible versions—AI assists in ensuring that these papers are transformed as accurately as feasible.

• Legal Documentation

Crucial approved legal papers, such as loan documentation, can be scanned and stored in an electronic database for convenient retrieval. The documents may also be viewed and shared by many people.

• Banking

You may snap a front and back photo of a cheque you want to deposit with your phone. The check may be automatically reviewed by AI-powered OCR technology to ensure it is legitimate and verifies the cash you wish to deposit.

OCR and AI: A Benefit to Businesses

Converting physical writing to digital required human labor; each page would have to be retyped, a time-consuming and error-prone job. The conversion takes less time using the OCR system and is more accurate than the original material. Users can modify, style, and search a page once OCR turns it into pdf format. They can also quickly share it through email, embed it in a webpage, and save it as zip files. 

This document interpretation capacity enables firms to study many documents without having to use human labor. Thus, reducing time-consuming admin duties is essential for increasing work engagement and lowering attrition. 

Learn OCR Today

According to researchers, demand for AI-powered OCR is expected to grow as these technologies become more productive and cost-effective. Check out Simplilearn's AI and machine learning lessons and training options, if you want to know more about OCR. Ranked No.1 AI and Machine Learning Course by TechGig in partnership with Purdue & IBM, this course will help you master Machine Learning, Deep Learning, Statistics, Reinforcement Learning, and NLP.