Date of Award

2012

Publication Type

Master Thesis

Degree Name

M.A.Sc.

Department

Electrical and Computer Engineering

Keywords

Applied sciences, Artificial neural networks, Devanagari, Discrete cosine transform, Handwriting recognition

Supervisor

Maher Sid-Ahmed

Supervisor

Majid Ahmadi

Rights

info:eu-repo/semantics/openAccess

Abstract

This thesis proposes a neural network based framework to classify online Devanagari characters into one of 46 characters in the alphabet set. The uniqueness of this work is three-fold: (1) The feature extraction is just the Discrete Cosine Transform of the temporal sequence of the character points (utilizing the nature of online data input). We show that if it is used right, a simple feature set yielded by the DCT can be very reliable for accurate recognition of Devanagari handwriting, (2) The mode of character input is through a computer mouse - training the system with which will lead to jitter-robustness, and (3) We have built the online handwritten database of Devanagari characters from scratch, and there are some unique features in the way we have built up the database. Lastly, after comprehensive testing of the algorithm on 2760 characters, recognition rates of up to 97.2% are achieved.

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