In this paper, we address some issues in developing an online handwritten character recognition(HCR) system for an Indian language script, Telugu. The number of characters in this script is estimated to be around 5000. A character in this script is written as a sequence of strokes. The set of strokes in Telugu consists of 253 unique strokes. As the similarity among several strokes is high, we propose a modular approach for recognition of strokes. Based on the relative position of a stroke in a character, the stroke set has been divided into three subsets, namely, baseline strokes, bottom strokes and top strokes. Classifiers for the different subsets of strokes are built using support vector machines(SVMs). We study the performance of the classifiers for subsets of strokes and propose methods to improve their performance. A comparative study using hidden Markov models(HMMs) shows that the SVM based approach gives a significantly better performance. © 2007 IEEE.

Chandra Sekhar C

Department of Computer Science and Engineering

A. Jayaraman

V.S. Chakravarthy

character recognition

Classification (of information)

Classifiers

Computer networks

Hidden Markov models

Learning systems

Markov processes

Rough Set Theory

DOCUMENT ANALYSIS

International conferences

MODULAR APPROACH

Support vector machines

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Modular Approach to Recognition of Strokes in Telugu Script

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

We combine the strengths of four different pattern analysis techniques to develop a powerful and efficient system for handwritten character recognition. The four techniques are: 1) Convolutional neural networks (CNN), 2) Principal Component Analysis (PCA), 3) Support vector machines, 4) Multiclassifier systems. The proposed system that embodies the above-mentioned four techniques is used for recognition of offline handwritten Telugu characters. Telugu aksharas of consonant-vowel (CV) type, with 36 consonant classes and 15 vowel modifier classes, are used for the study. Telugu dataset consisted of 47428 CV images in the training set and 5156 CV images in the test set. In addition to Telugu dataset, MNIST database consisting of 60000 digits for training and 10000 digits for testing was used in this study. The proposed system yields a performance of 98.5% on MNIST numeric data, 92.26% and 92% on consonants and vowel modifier of Telugu characters respectively. © 2013 IEEE.

2013 National Conference on Communications, NCC 2013

An efficient multiclassifier system based on convolutional neural network for offline handwritten Telugu character recognition

We present two schemas for online recognition of Telugu characters, involving elaborate multiclassifier architectures. Considering the three-tier vertical organization of a typical Telugu character, we divide the stroke set into 4 subclasses primarily based on their vertical position. Stroke level recognition is based on a bank of Support Vector Machines (SVMs), with a separate SVM trained on each of these classes. Character recognition for Schema 1 is based on a Ternary Search Tree (TST), while for Schema 2 it is based on a SVM. The two schemas yielded overall stroke recognition performances of 89.59% and 96.69% respectively surpassing some of the recent online recognition performance results related to Telugu script reported in literature. The schemas yield character-level recognition performances of 90.55% and 96.42% respectively. © 2012 IEEE.

Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR

Two schemas for online character recognition of Telugu script based on Support Vector Machines

A system for online recognition of handwritten Tamil characters is presented. A handwritten character is constructed by executing a sequence of strokes. A structure- or shape-based representation of a stroke is used in which a stroke is represented as a string of shape features. Using this string representation, an unknown stroke is identified by comparing it with a database of strokes using a flexible string matching procedure. A full character is recognized by identifying all the component strokes. Character termination, is determined using a finite state automaton. Development of similar systems for other Indian scripts is outlined. © 2004 IEEE.

Online handwriting recognition for Tamil

Eighth International Conference on Document Analysis and Recognition (ICDAR'05)

Arabic handwriting recognition using baseline dependant features and hidden Markov modeling

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

Principal component analysis for online handwritten character recognition

1995 International Conference on Acoustics, Speech, and Signal Processing

A discrete parameter HMM approach to on-line handwriting recognition

Proceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications, 2004.

Incremental support vector machines for handwritten arabic character recognition

Modular approach to recognition of strokes in Telugu script

Journal	Data powered by TypesetProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Publisher	Data powered by TypesetIEEE
ISSN	15205363
Open Access	No