Universiti Teknologi Malaysia Institutional Repository

Cursive script segmentation with neural confidence

Saba, Tanzila and Rehman, Amjad and Sulong, Ghazali (2011) Cursive script segmentation with neural confidence. International Journal Of Innovative Computing Information And Control, 7 (8). pp. 4955-4964. ISSN 1349-4198

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Official URL: http://www.ijicic.org/ijicic-10-03017.pdf

Abstract

This paper presents a new, simple and fast approach for character segmentation of unconstrained handwritten words. The proposed approach first seeks the possible character boundaries based on characters geometric features analysis. However, due to inherited ambiguity and a lack of context, few characters are over-segmented. To increase the efficiency of the proposed approach, an Artificial Neural Network is trained with significant number of valid segmentation points for cursive handwritten words. Trained neural network extracts incorrect segmented points efficiently with high speed. For fair comparison, benchmark database CEDAR is used. The experimental results are promising from complexity and accuracy points of view.

Item Type:Article
Uncontrolled Keywords:back propagation learning, character recognition, character segmentation, feature extraction, handwriting recognition
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:28882
Deposited By: Liza Porijo
Deposited On:30 Nov 2012 01:04
Last Modified:30 Nov 2012 01:04

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