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IPM
30
YEARS OLD

“School of Cognitive Sciences”

Paper   IPM / Cognitive Sciences / 15505
   School of Cognitive Sciences
  Title: Combining RtL and LtR HMMs to recognise handwritten Farsi words of small- and medium-sized vocabularies
  Author(s):
1 . A. Abbaszadeh Arani
2 . E. Kabir
3 . R. Ebrahimpour
  Status: Published
  Journal: IET Computer Vision
  Vol.: 12
  Year: 2018
  Pages: 925-932
  Supported by: IPM
  Abstract:
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

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