Industrial Consultancy & Sponsored Research (IC&SR) , IIT Madras
Technology Category/ Market
Technology: Method for recognition of handwritten Telegu Character;
Industry: Banking Sector, Security;
Applications: Banking Sector & others;
Market: The global optical character recognition market is projected to grow at a CAGR of 14.8%Â during 2023-2027.
Image Gallery
Problem Statement
- Generally, Handwritten character recognition can be performed either online or offline.
- Based on prior arts survey, a Hidden Markov Model based system is used for online character recognition in Telugu & report a top-l accuracy of 91.6%, but said system operates at symbol level, not at character level.
- Further there are other issues including accuracy to identify characters both in online and offline mode.
- Hence, there is a need to mitigate above challenges, and present invention provides the solution in efficient manner.
Technology
- The present invention describes a method & system for recognition of handwritten characters based on Convolutional Neural Networks (CNN).
- Each network comprises a first, second, third, & a fourth hidden layers of neurons connected to each other.
- The method of recognizing handwritten characters is depicted in the smart chart and figures.
- The handwritten characters are scanned into an input image & processed by Convolutional neural networks.
- Principal component analysis (PCA) system is to identify the output class to which the characters belong.
- The support vector mechanism (SVM) is configured to determine support vectors & identify the output class based on the determined support vectors, & also to train weight stage from the last hidden layer of the CNN to the output layer.
- The entire hierarchy of CNN layers from the input to the last hidden layer may then be considered as a kernel layer of the SVM.
Key Features/ Value Proposition
Technical Perspective:
- Data collected splits into training & testing data wherein 90% data used for training & remaining for testing.
- Training data divides into two sets & used to train nine different networks.
- A classifier is created by combining the networks with least correlation performance on the test dataset. Further, a new classifier pair yielded higher accuracy than individual classifiers.
- Facilitates the feature of supporting the recognition of handwritten not only Telegu character but also support other Indian regional language like Tamil, Kannada & Malayalam.
Industrial Perspective:
- User friendly with high accuracy & cost-effective system & method.
- Very fast process & consumes less time for recognition of Indian Language character (Telegu).
Questions about this Technology?
Contact For Licensing
sm-marketing@imail.iitm.ac.in
ipoffice2@iitm.ac.in
Research Lab
Prof. Srinivasa Chakravarthy VÂ Â
Department of Biotechnology
Intellectual Property
IITM IDF Ref. 1023
IN Patent No: 387357
Technology Readiness Level
TRL-3/4
 Proof of Concept ready, tested in lab