DocumentCode :
3489740
Title :
A Novel Baseline-independent Feature Set for Arabic Handwriting Recognition
Author :
Bing Su ; Xiaoqing Ding ; Liangrui Peng ; Changsong Liu
Author_Institution :
Dept. of Electron. Eng., Tsinghua Univ., Beijing, China
fYear :
2013
fDate :
25-28 Aug. 2013
Firstpage :
1250
Lastpage :
1254
Abstract :
HMM-based analytical methods have been widely used for Arabic handwriting recognition. A key factor influencing the performance of HMM-based systems is the features extracted from a sliding window. In this paper, we propose a novel baseline-independent feature set extracted from a wider sliding window to directly capture the contextual information. This feature set is a combination of center of mass based log-space distribution features and inverse percentile features. Center of mass based log-space distribution features use a normalized histogram to describe the distribution of foreground pixels in different direction and distances with respect to the center of mass. Experiments on the IFN/ENIT database demonstrate the effectiveness of the proposed feature set. Further, this feature set can be combined with some popular baseline-independent features to form a large feature set, which achieves comparable results with several state-of-the-art systems using a simple HMM-based architecture.
Keywords :
feature extraction; handwriting recognition; hidden Markov models; natural language processing; Arabic handwriting recognition; HMM-based analytical methods; feature extraction; foreground pixels; hidden Markov models; inverse percentile features; mass based log-space distribution features; normalized histogram; novel baseline-independent feature set; sliding window; Feature extraction; Handwriting recognition; Hidden Markov models; Histograms; Optical character recognition software; Shape; Training; Arabic handwriting recognition; baseline-independent; feature extraction; log-space distribution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition (ICDAR), 2013 12th International Conference on
Conference_Location :
Washington, DC
ISSN :
1520-5363
Type :
conf
DOI :
10.1109/ICDAR.2013.253
Filename :
6628814
Link To Document :
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