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.

Srinivasa Chakravarthy

Department Of Biotechnology

COMPOSITE CHARACTERS

INDIAN SCRIPTS

ONLINE HANDWRITNG RECOGNITION

SHAPE FEATURES

STROKE INDENTIFICATION

Automation

CHARACTER SETS

Finite automata

Graph theory

Image analysis

Linguistics

Online systems

Pattern matching

Optical character recognition

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

Online handwriting recognition for Tamil

Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR

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.

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

In this paper, we propose two approaches in a hybrid framework in which a Gaussian mixture model (GMM) based method is used to obtain a fixed length pattern representation for a varying length time series and then a discriminative model is used for classification. In score vector based approach, each time series in a training data set is modelled by a GMM. A log-likelihood score vector representation is obtained by applying a time series to all GMMs. In segment modelling based approach, a time series is segmented into fixed number of segments and a GMM is built for each segment. Parameters of GMMs of segments are concatenated to obtain a parametric vector representation. Support vector machine is used for classification of score vector representation and parametric vector representation of time series. The proposed approaches are studied for speech emotion recognition and audio clip classification tasks. Copyright © 2010 Inderscience Enterprises Ltd.

International Journal of Data Mining, Modelling and Management

Classification of varying length multivariate time series using Gaussian mixture models and support vector machines

Time series clustering finds applications in diverse fields of science and technology. Kernel based clustering methods like kernel K-means method need number of clusters as input and cannot handle outliers or noise. In this paper, we propose a density based clustering method in kernel feature space for clustering multivariate time series data of varying length. This method can also be used for clustering any type of structured data, provided a kernel which can handle that kind of data is used. We present heuristic methods to find the initial values of the parameters used in our proposed algorithm. To show the effectiveness of this method, this method is applied to two different online handwritten character data sets which are mutivariate time series data of varying length, as a real world application. The performance of the proposed method is compared with the spectral clustering and kernel k-means clustering methods. Besides handling outliers, the proposed method performs as well as the spectral clustering method and outperforms the kernel k-means clustering method. © 2008 IEEE.

Proceedings of the International Joint Conference on Neural Networks

A density based method for multivariate time series clustering in kernel feature space

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.

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

Modular approach to recognition of strokes in Telugu script

In this paper, we describe the design and implementation of a unifi ed word processing system to handle handwritten characters for Indian languages.It uses XML [1] based document format to store the strokes written by the user.It provides fi Iters to export the native document into other standards like InkML (Ink Markup Language) [2] and LDF (Lekhak Data Format). It integrates shape feature based recognition engine to recognise the whole document and spell checker to correct the wrongly recognised words. It includes the support to send the document as an attachment with E-mail without the need for recognition. © 2007 IEEE.

Proceedings - International Conference on Computing: Theory and Applications, ICCTA 2007

PATRAM: A unifi ed word processing system for handwritten characters in Indian languages

A handwritten character magically survives serious distortions in size, orientation and even structure, justifying perhaps its Sanskrit name-Aksharam, the undecaying. Several traditional approaches model characters in terms of certain shape features like line crossings, T-junctions etc. But there is no sanctity in the choice of these features-which may be specific to a script-nor is there a limit to their number. We address the general problem of defining the shape of a 2D line diagram, with character as a significant special case. To this end we develop a framework based on a branch of mathematics known as the Catastrophe theory. A small set of 11 shape features is derived systematically from our framework. The 11 features are found in several of world's scripts and may in fact be universal. More complex shapes break down to the above 11 in handwritten scripts. We discuss how our model can be applied to on-line character recognition from pen-based devices. © 2003 Elsevier Science B.V. All rights reserved.

Pattern Recognition Letters

The shape of handwritten characters

Pattern Recognition

Template-based online character recognition

Wiley Encyclopedia of Electrical and Electronics Engineering

Online Handwriting Recognition

The International Journal of Advanced Manufacturing Technology

Process development for ball grid array assembly using a design of experiments approach

Lecture Notes in Computer Science Neural Networks: Tricks of the Trade

Combining Neural Networks and Context-Driven Search for Online, Printed Handwriting Recognition in the Newton

Journal	Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR
ISSN	15505235
Open Access	No