Improving OCR-Degraded Arabic Text Retrieval Through an Enhanced Orthographic Query Expansion Model

Abstract

This paper introduces an Enhanced Orthographic Query Expansion Model for improving Text Retrieval of Arabic Text resulting from the Optical Character Recognition (OCR) process. The proposed model starts with checking the query word through two word based a word based error synthesizing sub-models then in a character N-Gram simulation sub-model. The model is flexible either to get the corrected word once it finds it from the early stages (in case of highest performance is needed) or to check all possibilities from all sub-models (in case of highest expansion is needed). The 1^st word based sub-model that has manual word alignment (degraded & original pairs) alone has high precision and recall but with some limitations that may affect recall (in case of connected multi-words as OCR output). The second words based sub-model provides high precession (less than the 1^st one) but also with higher recall. The last sub-model which is a character N-gram one, provides low precision but high recall. The output of the proposed orthographic query expansion model is the original query extended with the expected degraded words taken from the OCR errors simulation model. The proposed model gave a higher precision (97.5%) than all previous ones with keeping the highest previous recall numbers.