Attention Based RNN Model for Document Image Quality Assessment

Document Image Quality Assessment (DIQA) is an essential step preceding Optical Character Recognition (OCR). In this paper we propose an attention based Recurrent Neural Network (RNN) model for camera based DIQA. Convolutional Neural Network (CNN) and RNN are integrated into our model to capture spatial features for several glimpse regions step by step within an image patch. Reinforcement learning is adopted to train a locator to generate the optimal location of a glimpse region for the next time step so that attention can be payed to the salient part. Given an input document image, patches are generated with a sliding window, and the pure background ones are sifted out. Quality scores are obtained for all the sifted patches by applying the proposed attention based RNN method, and the patch scores are averaged over each input image as the result of DIQA. We conduct experiments on two public datasets and make comparisons with several other reported methods. Experimental results show that our model achieves the state of the art performance.