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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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • M. S. Khorsheed
    • 1
  1. 1.Computer & Electronics Research InstituteKing AbdulAziz City for Science and Technology (KACST)RiyadhSaudi Arabia

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