A post-processing scheme for malayalam using statistical sub-character language models

  • Authors:

  • Karthika Mohan;C. V. Jawahar

  • Affiliations:

  • Centre for Visual Information Technology, IIIT-Hyderabad, India;Centre for Visual Information Technology, IIIT-Hyderabad, India

  • Venue:
  • DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
  • Year:
  • 2010

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Abstract

Most of the Indian scripts do not have any robust commercial OCRs. Many of the laboratory prototypes report reasonable results at recognition/classification stage. However, word level accuracies are still poor. It is well known that word accuracy decreases as the number of characters in a word increase. For Malayalam, the average number of characters in a word is almost twice that of English. Moreover, the number of words required to cover 80% of the Malayalam language is more than forty times that of other Indian languages such as Hindi. Hence a direct dictionary based post-processing scheme is not suitable for Malayalam. In this paper, we propose a post-processing scheme which uses statistical language models at the sub-character level to boost word level recognition results. We use a multi-stage graph representation and formulate the recognition task as an optimization problem. Edges of the graph encode the language information and nodes represent the visual similarities. An optimal path from source node to destination node represents the recognized text. We validate our method on more than 10,000 words from a Malayalam corpus.