Holistic Urdu Handwritten Word Recognition Using Support Vector Machine

Authors:
Malik Waqas Sagheer;Chun Lei He;Nicola Nobile;Ching Y. Suen
Affiliations:
-;-;-;-
Venue:
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Year:
2010

Citing 0
Cited 3

TSV-LR: topological signature vector-based lexicon reduction for fast recognition of pre-modern Arabic subwords

Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques

ACM Transactions on Asian Language Information Processing (TALIP)
The optical character recognition of Urdu-like cursive scripts

Pattern Recognition

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Abstract

Since the Urdu language has more isolated letters than Arabic and Farsi, a research on Urdu handwritten word is desired. This is a novel approach to use the compound features and a Support Vector Machine (SVM) in offline Urdu word recognition. Due to the cursive style in Urdu, a classification using a holistic approach is adapted efficiently. Compound feature sets, which involves in structural and gradient features (directional features), are extracted on each Urdu word. Experiments have been conducted on the CENPARMI Urdu Words Database, and a high recognition accuracy of 97.00% has been achieved.