DocumentCode :
1737735
Title :
Holistic handwritten word recognition using discrete HMM and self-organizing feature map
Author :
Dehghan, M. ; Faez, K. ; Ahmad, M. ; Shridhar, M.
Author_Institution :
Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran
Volume :
4
fYear :
2000
fDate :
2000
Firstpage :
2735
Abstract :
A holistic system for the recognition of handwritten Farsi/Arabic words using right-left discrete hidden Markov models (HMM) and Kohonen self-organizing vector quantization is presented. The histogram of chain-code directions of the image strips, scanned from right to left by a sliding window, is used as feature vectors. The neighborhood information preserved in the self-organizing feature map (SOFM), was used for smoothing the observation probability distributions of trained HMMs. Experiments carried out on test samples show promising performance results
Keywords :
handwritten character recognition; hidden Markov models; optical character recognition; probability; self-organising feature maps; vector quantisation; Arabic words; Farsi words; Kohonen self-organizing vector quantization; chain-code directions; discrete HMM; discrete hidden Markov models; experiments; feature vectors; histogram; holistic handwritten word recognition; neighborhood information; performance results; probability distributions; self-organizing feature map; sliding window; Character recognition; Cities and towns; Feature extraction; Handwriting recognition; Hidden Markov models; Histograms; Image databases; Pattern recognition; Spatial databases; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 2000 IEEE International Conference on
Conference_Location :
Nashville, TN
ISSN :
1062-922X
Print_ISBN :
0-7803-6583-6
Type :
conf
DOI :
10.1109/ICSMC.2000.884410
Filename :
884410
Link To Document :
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