Abstract

Optical character recognition (OCR) accuracy of document images is an important factor for the success of many document processing and analysis tasks, especially for unconstraint captured document images. Although several document image OCR capability assessment methods are proposed, they mostly model the problem based on the empirically defined rules of image degradation, which cause the existing approaches infeasible for predicting the OCR scores. In this paper, a computational model is presented to automatically predict document image quality towards facilitating the OCR accuracy without references. Unlike conventional methods that use heuristically designed features, in our work the raw features are learned from training images and a generative quality model is built based on latent Dirichlet allocation, which is used to assess the document's OCR capability. We present evaluation results on a …

Date

August 23, 2015

Authors

Xujun Peng, Huaigu Cao, Prem Natarajan

Conference

2015 13th International Conference on Document Analysis and Recognition (ICDAR)

Pages

771-775

Publisher

IEEE