IET Computer Vision (Sep 2018)

Combining RtL and LtR HMMs to recognise handwritten Farsi words of small‐ and medium‐sized vocabularies

  • Seyed Ali Asghar Abbaszadeh Arani,
  • Ehsanollah Kabir,
  • Reza Ebrahimpour

DOI
https://doi.org/10.1049/iet-cvi.2017.0645
Journal volume & issue
Vol. 12, no. 6
pp. 925 – 932

Abstract

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In this study, a method for holistic recognition of handwritten Farsi words is proposed, which fuses the outputs of right‐to‐left (RtL) and left‐to‐right (LtR) hidden Markov models (HMMs). The experimental results on 16,000 images of 200 names of Iranian cities, from the ‘Iranshahr 3’ are presented and compared with those methods using only RtL or LtR models. Experimental results show that the main sources of error are similar beginnings or similar endings of the words. Since RtL and LtR models when dealing with the words behave differently, there is notable error diversity between the two classifiers in such a way that their combination increases the recognition rate. Compared to the RtL‐HMM, the product of output scores of the RtL and LtR‐HMMs reduces the classification error to about 6, 6 and 3%, for three different feature sets. A subjective error analysis on the results is also provided.

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