An Off-Line Cursive Handwriting Recognition System
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Role of Holistic Paradigms in Handwritten Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Bangla Handwritten Text into Characters by Recursive Contour Following
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Detecting Dominant Point on On-line Scripts with a Simple Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwriting Segmentation of Unconstrained Oriya Text
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Comparison of Elastic Matching Algorithms for Online Tamil Handwritten Character Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Recognition and Verification of Unconstrained Handwritten Words
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tree Structure forWord Extraction from Handwritten Text Lines
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A Two-stage Online Handwritten Chinese Character Segmentation Algorithm Based on Dynamic Programming
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Word Extraction from On-Line Handwritten Text Lines
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
IEICE - Transactions on Information and Systems
Character Recognition Systems: A Guide for Students and Practitioners
Character Recognition Systems: A Guide for Students and Practitioners
A Fuzzy Technique for Segmentation of Handwritten Bangla Word Images
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
A SVM-based cursive character recognizer
Pattern Recognition
Hidden Markov Models for Online Handwritten Tamil Word Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Online Handwritten Japanese Character String Recognition Incorporating Geometric Context
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Direction Code Based Features for Recognition of Online Handwritten Characters of Bangla
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
HMM-Based Online Handwriting Recognition System for Telugu Symbols
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Segmentation of On-Line Freely Written Japanese Text Using SVM for Improving Text Recognition
IEICE - Transactions on Information and Systems
Effect of Improved Path Evaluation for On-line Handwritten Japanese Text Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Online Handwritten Japanese Character String Recognition Using Conditional Random Fields
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A robust model for on-line handwritten japanese text recognition
International Journal on Document Analysis and Recognition - Special Issue DRR09
Online Bangla Word Recognition Using Sub-Stroke Level Features and Hidden Markov Models
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Creation of a Huge Annotated Database for Tamil and Kannada OHR
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Lexicon-Free, Novel Segmentation of Online Handwritten Indic Words
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Language models for online handwritten Tamil word recognition
Proceeding of the workshop on Document Analysis and Recognition
Global and local features for recognition of online handwritten numerals and Tamil characters
Proceedings of the 4th International Workshop on Multilingual OCR
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In this article, we propose a lexicon-free, script-dependent approach to segment online handwritten isolated Tamil words into its constituent symbols. Our proposed segmentation strategy comprises two modules, namely the (1) Dominant Overlap Criterion Segmentation (DOCS) module and (2) Attention Feedback Segmentation (AFS) module. Based on a bounding box overlap criterion in the DOCS module, the input word is first segmented into stroke groups. A stroke group may at times correspond to a part of a valid symbol (over-segmentation) or a merger of valid symbols (under-segmentation). Attention on specific features in the AFS module serve in detecting possibly over-segmented or under-segmented stroke groups. Thereafter, feedbacks from the SVM classifier likelihoods and stroke-group based features are considered in modifying the suspected stroke groups to form valid symbols. The proposed scheme is tested on a set of 10000 isolated handwritten words (containing 53,246 Tamil symbols). The results show that the DOCS module achieves a symbol-level segmentation accuracy of 98.1%, which improves to as high as 99.7% after the AFS strategy. This in turn entails a symbol recognition rate of 83.9% (at the DOCS module) and 88.4% (after the AFS module). The resulting word recognition rates at the DOCS and AFS modules are found to be, 50.9% and 64.9% respectively, without any postprocessing.