Utilising Speech Recognition System Capabilities To Recognise Cursive Arabic Text

摘要

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 200 A4-size sheets typewritten in two computer-generated fonts is used to assess the performance of the proposed system.