Abstract

Automatically accessing information from unconstrained image documents has important applications in business and government operations. These real-world applications typically combine optical character recognition (OCR) with language and information technologies, such as machine translation (MT) and keyword spotting. OCR output has errors and presents unique challenges to late-stage processing. This paper addresses two of these challenges: (1) translating the output from Arabic handwriting OCR which lacks reliable sentence boundary markers, and (2) searching named entities which do not exist in the OCR vocabulary, therefore, completely missing from Arabic handwriting OCR output. We address these challenges by leveraging natural language processing technologies, specifically conditional random field-based sentence boundary detection and out-of-vocabulary (OOV) name detection …

Date

2015

Authors

Jinying Chen, Huaigu Cao, Premkumar Natarajan

Journal

International Journal on Document Analysis and Recognition (IJDAR)

Volume

18

Pages

235-247

Publisher

Springer Berlin Heidelberg