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Fusion of HMM Classifiers, Based on x, y and (x, y) Signals, for the Recognition of Online Farsi Handwriting: a Large Lexicon Approach

  • Research Article - Computer Engineering and Computer Science
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Abstract

Online handwriting is basically formed by a combination of horizontal and vertical trajectories. We employed decision fusion of semi-continuous hidden Markov model (HMM), classifiers, designed for x, y and (x, y) signals separately, to increase the recognition rate of online Farsi subwords. Because of large lexicon and few training samples for some classes, an embedded method was used to train HMM models of subwords. The result of segmentation of (x, y) signal was exploited to segment x and y signals to letters or sub-letters in the training phase. Different decision fusions of the three classifiers were investigated. The best result was achieved by applying the product rule to combine y and (x, y) classifiers.

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Authors and Affiliations

  1. Department of Electrical Engineering, Islamic Azad University, Semnan Branch, Semnan, Iran

    Vahid Ghods

  2. Department of Electrical and Computer Engineering, Tarbiat Modarres University, Tehran, Iran

    Ehsanollah Kabir

  3. Department of Electrical and Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

    Farbod Razzazi

Authors
  1. Vahid Ghods
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  2. Ehsanollah Kabir
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  3. Farbod Razzazi
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Correspondence to Vahid Ghods.

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Ghods, V., Kabir, E. & Razzazi, F. Fusion of HMM Classifiers, Based on x, y and (x, y) Signals, for the Recognition of Online Farsi Handwriting: a Large Lexicon Approach. Arab J Sci Eng 39, 1713–1723 (2014). https://doi.org/10.1007/s13369-013-0747-2

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  • DOI: https://doi.org/10.1007/s13369-013-0747-2

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