Data Science and Optical Character Recognition Systems

The recent development in machine learning technologies influenced the ML based application in terms of performance, accuracy, and robustness.  

As we know the volume of digitized documents is increasing day by day worldwide, so also the demands for automatic processing and information extraction from the image documents. Numerous projects are initiated by government and private agencies for the generation of digital document repositories for the preservation of heritage and information. Various libraries, institutes and organizations are seeking the digitization of documents they have. Some of them are focusing on the extraction of relevant data for further analysis and business growth, whereas some of them are interested in the preservation of heritage and information archival. The goal of digitization is to make usable existing data present in various formats and create data extract for document management and managing the records. These projects recursively generate huge number of scanned documents which require further processing for information extraction, automatic archival/indexing and further analysis. Handling and processing of these document images must require automatic routines to process and extract the data. These routines generally employ digital image processing and artificial intelligence-based algorithms to realize the objectives of information processing and extraction.

Document image understanding comprises of various algorithms or methodologies for different problems like document images may be collected from the various sources like printed scanned images, certificates, newspaper/magazine images, scene text images etc. Each of these categories possess different challenges and output requirements. The Document image understanding (DIU) system processes the data and generates the output as per the requirements. The DIU offers various solutions to process document images and focus the concern with Optical character recognition systems (OCR).

In terms of Optical recognition system (OCR) which is basically used to recognize text in the images and convert it to machine readable format for further processing. OCR is a heart of document image processing because this module extracts and recognition text data from the images. Traditional systems are designed in phases and consisting majorly of image pre-processing, segmentation, feature extraction, recognition engine and post processing modules. These recognition engines employ generally supervised classification classes of learning and performed with machine learning models like Support Vector Machine, Random Forest Decision Trees, feed forwards NN. The overall accuracy of the traditional systems depends on the performance of each module. Moreover, the error at the pre-processing or segmentation is propagated to next stages till the generation of recognized text. Unlike previous systems, it can scan huge areas for long stretches of time. 

To extract insights, robust analytical tools are needed. The best data science courses offered by universities in Gurgaon realizing the increasing use of Data Science and AIML techniques to extract insights from images withing the documents for different applications. They are inculcating the importance of data visualization and analytical skills through their data   science course curriculum to students, aiming to provide them robust platform for their careers in this field. Best data science universities in Gurgaon uses project based learning and advance certification and training program for data driven analysis and OCR techniques for various applications. Students are advancing in this field and choosing their higher studies in this field.

Authored By

Dr. Poonam Chaudhary
Associate Professor, CSE, NCU