JOURNAL ARTICLE

Off-line cursive handwritten Tamil character recognition

R. Jagadeesh KannanR. Prabhakar

Year: 2008 Journal:   WSEAS Transactions on Signal Processing archive Vol: 4 (6)Pages: 351-360

Abstract

In spite of several advancements in technologies pertaining to Optical character recognition, handwriting continues to persist as means of documenting information for day-to-day life. The process of segmentation and recognition pose quiets a lot of challenges especially in recognizing cursive handwritten scripts of different languages. The concept proposed is a solution crafted to perform character recognition of hand-written scripts in Tamil, a language having official status in India, Sri Lanka, and Singapore. The approach utilizes discrete Hidden Markov Models (HMMs) for recognizing off-line cursive handwritten Tamil characters. The tolerance of the system is evident as it can overwhelm the complexities arise out of font variations and proves to be flexible and robust. Higher degree of accuracy in results has been obtained with the implementation of this approach on a comprehensive database and the precision of the results demonstrates its application on commercial usage. The methodology promises to present a simple and fast scaffold to construct a full OCR system extended with suitable pre-processing.

Keywords:
Tamil Cursive Computer science Scripting language Hidden Markov model Optical character recognition Character (mathematics) Artificial intelligence Speech recognition Handwriting Segmentation Handwriting recognition Natural language processing Intelligent character recognition Font Pattern recognition (psychology) Character recognition Feature extraction Linguistics Mathematics

Metrics

9
Cited By
1.18
FWCI (Field Weighted Citation Impact)
16
Refs
0.85
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Handwritten Text Recognition Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Vehicle License Plate Recognition
Physical Sciences →  Engineering →  Media Technology
Image Retrieval and Classification Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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