A Post-Processing Scheme for Malayalam using Statistical Sub-character Language Models - Karthika Mohan and C. V. Jawahar (ACM, 2010)
Most of the Indian scripts do not have any robust commer- cial OCRs. Many of the laboratory prototypes report rea- sonable 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 char- acters in a word is almost twice that of English. Moreover, the number of words required to cover 80% of the Malay- alam 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 simi- larities. 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.