Muthiah Annamalai

Muthiah Annamalai

We report generation of a MNIST [4] compatible data set [1] for Tamil vowels to enable building a classification DNN or other such ML/AI deep learning [2] models for Tamil OCR/Handwriting applications. We report the capability of the 60,000 grayscale, 28x28 pixel dataset to build a 92% accuracy (training) and 82% cross-validation 4-layer CNN, with 100,000+ parameters, in TensorFlow. We also report a top-1 classification accuracy of 70% and top-2 classification accuracy of 92% on handwritten vowels showing, for the same network.

Muthiah Annamalai, T. Shrinivasan

Tamil language has an agglutinative, diglossic, alpha-syllabary structure which provides a significant combinatorial explosion of morphological forms all of which are effectively used in Tamil prose, poetry from antiquity to the modern age in an unbroken chain of continuity. However, for the language understanding, spelling correction purposes some of these present challenges as out-of-dictionary words. In this paper the authors propose algorithmic techniques to handle specific problems of conjoined-words (out-of-dictionary) (transliteration)[thendRalkattRu] = [thendRal]+[kattRu] when parts are alone present in word-list in efficient way. Morphological structure of Tamil makes it necessary to depend on synthesis-analysis approach and dictionary lists will never be sufficient to truly capture the language. In this paper we have attempted to make a summary of various known algorithms for specific classes of Tamil spelling errors. We believe this collection of suggestions to improve future spelling checkers. We also note do not cover many important techniques like affix removal and other such techniques of key importance in rule-based spell checkers.