Attention Feedback and Representations in OCR

Abstract

A Kannada OCR, named Lipi Gnani, has been designed and developed from scratch, with the motivation of it being able to convert printed text or poetry in Kannada script, without any restriction on vocabulary. The training and test sets have been collected from over 35 books published between the period 1970 to 2002, and this includes books written in Halegannada and pages containing Sanskrit slokas written in Kannada script. The coverage of the OCR is nearly complete in the sense that it recognizes all the punctuation marks, special symbols, Indo-Arabic and Kannada numerals and also the interspersed English words. Several minor and major original contributions have been done in developing this OCR at the different processing stages such as binarization, line and character segmentation, recognition and Unicode mapping. This has created a Kannada OCR that performs as good as, and in some cases, better than the Google s Tesseract OCR, as shown by the results. To the knowledge of the authors, this is the maiden report of a complete Kannada OCR, handling all the issues involved. Currently, there is no dictionary based postprocessing, and the obtained results are due solely to the recognition process. Four benchmark test datasets containing scanned pages from books in Kannada, Sanskrit, Konkani and Tulu languages, but all of them printed in Kannada script, have been created, along with the ground truth in Unicode. The word level recognition accuracy of Lipi Gnani is 5.3% higher on the Kannada dataset than that of Google s Tesseract OCR, 8.5% higher on the Sanskrit dataset, and 23.4% higher on the datasets of Konkani and Tulu. Inspired by the rich feedback that exists in the visual neural pathway that is active during the recognition process, we have proposed the use of feedback from the latter modules in the OCR workflow, such as recognition and Unicode generation, to the earlier stages such as binarization and segmentation, to result in the overall improvement of the performance of the OCR on old documents...