HOLISTIC FARSI HANDWRITTEN WORD RECOGNITION USING GRADIENT FEATURES

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IMANI Z.; AHMADYFARD A.R.; ZOHREVAND A.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

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Paper Information

Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2016 | Volume:4 | Issue:1 Page(s): 19-25

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Information Journal Paper

Title

HOLISTIC FARSI HANDWRITTEN WORD RECOGNITION USING GRADIENT FEATURES

Author(s)

IMANI Z. | AHMADYFARD A.R. | ZOHREVAND A. | Issue Writer Certificate

Keywords

HANDWRITTEN WORD RECOGNITIONQ2

DIRECTIONAL GRADIENT FEATUREQ2

INTENSITY GRADIENT FEATUREQ2

HIDDEN MARKOV MODELQ2

SELF-ORGANIZING FEATURE MAPQ2

FARSA DATABASEQ2

Abstract

In this paper we address the issue of recognizing Farsi handwritten words. Two types fo gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and INTENSITY GRADIENT FEATUREs. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete HIDDEN MARKOV MODEL (HMM). To evaluate the performance of the proposed method, FARSA dataset has been used. The experimental results show that the proposed system, applying DIRECTIONAL GRADIENT FEATUREs, has achieved the recognition rate of 69.07% and outperformed all other existing methods.

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APA: Copy

IMANI, Z., AHMADYFARD, A.R., & ZOHREVAND, A.. (2016). HOLISTIC FARSI HANDWRITTEN WORD RECOGNITION USING GRADIENT FEATURES. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, 4(1), 19-25. SID. https://sid.ir/paper/255387/en

Vancouver: Copy

IMANI Z., AHMADYFARD A.R., ZOHREVAND A.. HOLISTIC FARSI HANDWRITTEN WORD RECOGNITION USING GRADIENT FEATURES. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING[Internet]. 2016;4(1):19-25. Available from: https://sid.ir/paper/255387/en

IEEE: Copy

Z. IMANI, A.R. AHMADYFARD, and A. ZOHREVAND, “HOLISTIC FARSI HANDWRITTEN WORD RECOGNITION USING GRADIENT FEATURES,” JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, vol. 4, no. 1, pp. 19–25, 2016, [Online]. Available: https://sid.ir/paper/255387/en

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