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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Khorsheed, M. S. (2006). Mono-font cursive arabic text recognition using speech recognition system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4109 LNCS, pp. 755–763). Springer Verlag. https://doi.org/10.1007/11815921_83
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