Mono-font cursive arabic text recognition using speech recognition system

Authors:
M. S. Khorsheed
Affiliations:
Computer & Electronics Research Institute, King AbdulAziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
Venue:
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2006

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Abstract

This paper presents a system to recognise cursive Arabic typewritten text. The system is built using the Hidden Markov Model Toolkit (HTK) which is a portable toolkit for speech recognition system. The proposed system decomposes the page into its text lines and then extracts a set of simple statistical features from small overlapped windows running through each text line. The feature vector sequence is injected to the global model for training and recognition purposes. A data corpus which includes Arabic text of more than 100 A4–size sheets typewritten in Tahoma font is used to assess the performance of the proposed system.