Merging clustering and classification results for whole book recognition

Historical printed books OCR is one of the challenging tasks in the area of document image analysis. Low quality of print and paper and unfamiliar font faces are the most known problems. However, redundancy of word and sub-word occurrences in the document can be used to improve the recognition results. In this paper, we propose a highly accurate recognition system for printed old books. We use the combination of sub-word clustering and a LSTM neural network as a character recognizer to reduce the error rate. Due to the lack of information about the font faces, we manually label some part of the books. We show that the recognition error rate can be reduced noticeably by combining the results of the LSTM recognizer and sub-word clustering and rejecting the cases where the two methods differ in their prediction.