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Offline handwriting recognition using Artificial Neural Network and Hidden Markov Model

Tay, Yong Haur (2002) Offline handwriting recognition using Artificial Neural Network and Hidden Markov Model. PhD thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering.

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Abstract

Cursive handwriting is the most natural way for humans to communicate and record information. The developments of automatic systems that are capable of recognizing human handwritings offer a new way of improving human-computer interface and of enabling computers to perform repetitive tasks of reading and processing handwritten documents more efficiently. The aim of this thesis is to design an offline handwritten word recognition system based on the hybrid of Artificial Neural Network (ANN) and Hidden Markov Model (HMM). The Input space segmentation (INSEG) approach proposes various ways to segment word into characters. This approach creates the problem of junks - character hypotheses that are not true characters. Two training approaches have been introduced, namely character level discriminant training and word-level discriminant training. The latter shows integration of the ANN and HMM by using the gradient descent algorithm. Different topologies of the ANN have been investigated for modeling of junks. Three isolated word databases, namely, IRONOFF, AWS and SRTP, have been used as the evaluation of the proposed system. Experimental results have shown that the ANN-HMM hybrid with word-level discriminant training consistently yield better recognition accuracy compared to character level discriminant training and discrete HMM-based recognition system. It achieves recognition accuracy of 97.3%, 88.4%, 90.5% and 95.8%, on IRONOFF-1 96, IRONOFF-1 991, SRTP-Cheque, and AWS, respectively.

Item Type:Thesis (PhD)
Additional Information:Thesis (Doctor of Philosophy) - Universiti Teknologi Malaysia, 2002; Supervisor : Prof. Dr. Marzuki b. Khalid
Uncontrolled Keywords:handwritten word recognition, Neural Network, Hidden Markov Model (HMM), optical character recognition devices, optical pattern recognition, artificial intelligence
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Electrical Engineering
ID Code:4393
Deposited By: Ms Zalinda Shuratman
Deposited On:02 Oct 2007 01:53
Last Modified:04 Sep 2012 04:51

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