Offline Arabic handwriting recognition system based on HMM

Author

Xiang, Dong ; Yan, Huahua ; Chen, Xianqiao ; Cheng, Yanfen

Author_Institution

Sch. of Comput. Sci. & Technol., Wuhan Univ. of Technol., Wuhan, China

Volume

1

fYear

2010

fDate

9-11 July 2010

Firstpage

526

Lastpage

529

Abstract

Great challenges are faced in the offline recognition of cursive Arabic handwriting. This paper presents a segmentation-free system based on Hidden Markov Model (HMM) to handle this problem, where character segmentation stage is avoided prior to recognition. The system first extracts a set of robust features on binary handwritten images by sliding windows. Then the proposed system builds character HMM models and learns word HMM models using embedded training. Finally, best word maximizing the a posteriori is located through Viterbi Algorithm. Experiments that have been implemented on the benchmark IFN/ENIT database show the average recognition rate of this system is 84.09%.

Keywords

handwriting recognition; hidden Markov models; image segmentation; HMM; Viterbi algorithm; binary handwritten images; character segmentation; cursive Arabic handwriting; embedded training; hidden Markov model; offline Arabic handwriting recognition system; segmentation-free system; sliding windows; Books; Hidden Markov models; Image segmentation; Arabic; hidden markov model; offline handwritten; pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-5537-9

Type

conf

DOI

10.1109/ICCSIT.2010.5564429

Filename

5564429