Camera captured DIQA with linearity and monotonicity constraints

Abstract

Document image quality assessment (DIQA), which predicts the visual quality of the document images, can not only be applied to estimate document’s optical character recognition (OCR) performance prior to any actual recognition, but also provides immediate feedback on whether the documents meet the quality requirements for other high level document processing and analysis tasks. In this work, we present a deep neural network (DNN) to accomplish the DIQA task, where a Saimese based deep convolutional neural network (DCNN) is employed with customized losses to improve system’s capability of linearity and monotonicity to predict the quality of document images. Based on the proposed network along with the new losses, the obtained DCNN achieves the state-of-the-art quality assessment performance on the public datasets. The source codes and pre-trained models are available at https://gitlab.com/xujun.peng/DIQA-linearity-monotonicity.