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

• Published in: IET computer vision Vol. 12; no. 6; pp. 925 - 932
• Main Authors: Abbaszadeh Arani, Seyed Ali Asghar, Kabir, Ehsanollah, Ebrahimpour, Reza
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.09.2018; Wiley
• Subjects: classification error; combined RtL-LtR HMMs; error diversity; handwriting recognition; handwritten Farsi word recognition; hidden Markov models; image classification; Iranian cities; Iranshahr 3; left-to-right hidden Markov models; medium-sized vocabularies; natural language processing; Research Article; right-to-left hidden Markov models; small-sized vocabularies; subjective error analysis; handwritten Farsi word recognition; image classification; natural language processing; error diversity; medium-sized vocabularies; Iranian cities; small-sized vocabularies; handwriting recognition; hidden Markov models; Iranshahr 3; right-to-left hidden Markov models; subjective error analysis; left-to-right hidden Markov models; combined RtL-LtR HMMs; classification error
• ISSN: 1751-9632; 1751-9640; 1751-9640
• DOI: 10.1049/iet-cvi.2017.0645

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.