Handwritten Digit String Recognition for Indian Scripts

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12047)


In many documents digits/numerals may touch each other and hence digit string recognition is necessary as segmentation of individual numeral from the touching string is difficult. In this paper, we propose a digit string recognition system for four Indian popular scripts. Here we consider strings of Kannada, Oriya, Tamil and Telugu scripts for our experiment. This paper has two contributions: (i) we have developed 4 datasets of digit string for each of these four scripts. Each dataset has 20000 numeral string samples for training and 30000 samples for testing. As there is no such dataset available, it will be helpful to the community (ii) we apply a RNN free CNN (Convolutional Neural Network) and CTC (Connectionist Temporal Classifica-tion) based architecture for numeral string recognition. Unlike normal text string, in string of digits has no contextual information among the digits and hence a digit may be followed by an arbitrary digit in a digit string. Because of such behaviors we apply a CNN and CTC based architecture without RNN for numeral string recognition. We tested our scheme on our different test datasets and results are provided.


String recognition Convolutional Neural Network Connectionist Temporal Classification Postal Automation 


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Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina
  2. 2.CVPR UnitIndian Statistical InstituteKolkataIndia

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