Recognizing Cursive Arabic Text:Using statistical features and interconnected mono-HMMs

摘要：

This paper presents a cursive Arabic text recognition system. The system decomposes the document image into test line images and extracts a set of simple statistical features from a one-pixel width window which is sliding a cross that text line. It then injects the resulting feature vectors to Hidden Markov Models. The proposed system is applied to a data corpus which includes Arabic text of more than 600 A4-size sheets typewritten in multiple computergenerated fonts.

关键词： Document analysis pattern recognition Arabic text recognition hidden Markov models HTK

作者: M S Khorsheed H Al-Omari

作者单位: Image Processing and Signal Analysis & Recognition (IPSAR) Research Group, Computer Research Institute, King Abdulaziz City for Science and Technology PO Box 6086, Riyadh 11442, Saudi Arabia

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1561-1564

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）