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Lexicon reduction for printed Farsi subwords using pictorial and textual dictionaries

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Abstract

In this paper, we present a method to reduce the lexicon size of printed Farsi subwords, which utilizes the holistic shape along with the key character information to dynamically reduce the size of lexicon organized accordingly in two approaches: (1) based on the global shape description (to build a pictorial dictionary) and (2) based on constitutive character information (to build a textual dictionary). Given an input word image, the reduction procedure is accomplished in two successive stages. First, characteristic loci features are extracted and compared with the pictorial dictionary to select the candidate subwords based on their shapes similarity. The lexicon is further reduced in the second stage by determining the key character in the input image and comparing it with the textual dictionary. The key characters are defined as the ones which can be segmented and recognized rather easily and also, together with global descriptors, characterize the word image efficiently. A method for optimal selection of key characters is also proposed which is based on the mutual information of pictorial and textual dictionaries. The final candidate subwords are those sharing the same key character with the input image. The performance of the proposed method was studied experimentally on a set of 5,000 subword samples. The results obtained show a reduction rate of 97.83 % on a lexicon of 6,900 printed Farsi subwords.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Homa Davoudi

  2. Department of Electrical Engineering, Tarbiat Modares University, Tehran, Iran

    Ehsanollah Kabir

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  1. Homa Davoudi
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  2. Ehsanollah Kabir
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Correspondence to Homa Davoudi.

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Davoudi, H., Kabir, E. Lexicon reduction for printed Farsi subwords using pictorial and textual dictionaries. IJDAR 17, 359–374 (2014). https://doi.org/10.1007/s10032-014-0223-x

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  • DOI: https://doi.org/10.1007/s10032-014-0223-x

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