Effect of delayed strokes on the recognition of online Farsi handwriting

Authors:
Vahid Ghods;Ehsanollah Kabir;Farbod Razzazi
Affiliations:
Department of Electrical and Computer Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran;Department of Electrical and Computer Engineering, Tarbiat Modarres University, Tehran, Iran;Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Venue:
Pattern Recognition Letters
Year:
2013

Citing 10
Cited 0

On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
Lexicon Reduction in an HMM-Framework Based on Quantized Feature Vectors

ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Lexical Post-Processing Optimization for Handwritten Word Recognition

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Online Handwritten Script Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Fuzzy Approach to Recognition of Online Persian Handwriting

ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Lexicon reduction using dots for off-line Farsi/Arabic handwritten word recognition

Pattern Recognition Letters
On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm

Engineering Applications of Artificial Intelligence
Hierarchical On-line Arabic Handwriting Recognition

ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Feature Extraction for Online Farsi Characters

ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Decision fusion of horizontal and vertical trajectories for recognition of online Farsi subwords

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.10

Visualization

Abstract

Online handwriting recognition, OHR, has gained a widespread use in everyday life. In some scripts such as Farsi and Arabic, additional strokes are written after the main stroke. These delayed strokes include dots and small signs. In this paper, the delayed strokes effect was studied from two points of views: subword modeling and lexicon reduction. The model of a subword was made of concatenating the main body model and the delayed strokes models. Hidden Markov model, HMM, was employed as a classifier. The delayed strokes of an input subword were additionally exploited to reduce the lexicon size. Our proposed method was tested on TMU-OFS dataset, including 1000 online Farsi subwords, and a recognition rate of 85.2% was achieved.