  Since the low quality of document images will greatly undermine the chances
of success in automatic text recognition and analysis, it is necessary to
assess the quality of document images uploaded in online business process, so
as to reject those images of low quality. In this paper, we attempt to achieve
document image quality assessment and our contributions are twofold. Firstly,
since document image quality assessment is more interested in text, we propose
a text line based framework to estimate document image quality, which is
composed of three stages: text line detection, text line quality prediction,
and overall quality assessment. Text line detection aims to find potential text
lines with a detector. In the text line quality prediction stage, the quality
score is computed for each text line with a CNN-based prediction model. The
overall quality of document images is finally assessed with the ensemble of all
text line quality. Secondly, to train the prediction model, a large-scale
dataset, comprising 52,094 text line images, is synthesized with diverse
attributes. For each text line image, a quality label is computed with a
piece-wise function. To demonstrate the effectiveness of the proposed
framework, comprehensive experiments are evaluated on two popular document
image quality assessment benchmarks. Our framework significantly outperforms
the state-of-the-art methods by large margins on the large and complicated
dataset.
