Blog: OCR Algorithm: Improve and Automate Business Processes
Businesses of mid and large scale have massive amounts of printed documents in daily use. Among them are invoices, receipts, corporate documents, reports, media releases. And millions of them can be handwritten, which makes documents understandable for humans but difficult to read for machines.
Basic Concept of OCR
Optical character recognition (OCR) algorithms allow computers to analyze printed or handwritten documents automatically and prepare text data into editable formats for computers to efficiently process them. It is another way to extract and leverage business-critical data. According to the International Institute of Analytics, businesses using data can get a competitive advantage and see $430 billion in productivity benefits by the year 2020.
How It Works
Human eyes naturally recognize various patterns, fonts or styles. For computers, it is hard work to do. Any scanned document is a graphics file, i.e., a pattern of pixels. A computer localizes, detects and recognizes characters on an image and turns the image of paper documents into a text file.
Then, it becomes possible to extract meaningful information. Texts in a machine-readable form can then be used for different purposes. They can be scanned in search of patterns and vital data, used to generate reports and draw up charts, distributed into spreadsheets, and more.
6 Steps to Build an OCR Engine
Building an OCR engine from scratch, like those that InData Labs’ OCR experts work on, is a step-by-step process. The development process usually encompasses 6 steps needed to train an algorithm for efficient problem-solving with the help of optical character recognition.
1. Image Acquisition
The first step is to acquire images of paper documents with the help of optical scanners. This way, an original image can be captured and stored. Most of the paper documents are black and white, and an OCR scanner should be able to threshold images. In other words, it should replace each pixel in an image with a black or a white pixel. It is a method of image segmentation.
The goal of preprocessing is to make raw data usable by computers. The noise level on an image should be optimized and areas outside the text removed. Preprocessing is especially vital for recognizing handwritten documents that are more sensitive to noise. Preprocessing allows obtaining a clean character image to yield better results of image recognition.
The process of segmentation is aimed at grouping characters into meaningful chunks. There can be predefined classes for characters. So, images can be scanned for patterns that match the classes.
4. Feature Extraction
This step means splitting the input data into a set of features, that is, to find essential characteristics that make one or another pattern recognizable. As a result, each character gets classified in a particular class.
5. Training a Neural Network
Once all the features are extracted, they can be fetched to a neural network (NN) to train it to recognize characters. A training dataset and the methods applied to achieve the best output will depend on a problem that requires an OCR-based solution.
This stage is the process of refinement as an OCR model can require some corrections. However, it isn’t possible to achieve 100% recognition accuracy. The identification of characters heavily depends on the context. The verification of the output requires a human-in-the-loop approach.
The usage of OCR makes it easy to meet in-house document standards, give a head start to workflow automation, fully or partially eliminate the need for paper workflow. High-level optical character recognition services can assist many mid- and large-scale companies to make a profit from using custom-tailored algorithms. Industries like banking and finance, healthcare, tourism, and logistics may benefit the most from the successful implementation of OCR.
This article was originally published at InData Labs Blog: OCR Algorithm: Improve and Automate Business Processes