An Impact of OCR Errors on Automated Classification of OCR Japanese Texts with Parts-of-Speech Analysis

The technology of Optical Character Recognition (OCR) is used to generate texts in the process of digitizing print documents. Usually these texts need to be indexed and organized to simplify their access and retrieval. One of the powerful approaches in accomplishing this task is the use of Automated Text Classification. However, it is currently impossible for OCR technology to recognize all characters with an accuracy of 100%. We therefore propose the use of combined linguistic features in automated classification of OCR texts to formulate an informative feature set. The proposed method was experimentally evaluated using Japanese OCR texts. Empirical results indicate that the combination of linguistic features improved classification performance of OCR texts.