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 public dataset which have been captured using digital cameras with different level of blur degradation. The experimental results show that the proposed method outperforms traditional document image quality assessment approaches.
['Xujun Peng', 'Huaigu Cao', 'Prem Natarajan']
Document image OCR accuracy prediction via latent Dirichlet allocation