Document Image Quality Assessment via Explicit Blur and Text Size Estimation.

We introduce a novel two-stage system for document image quality assessment (DIQA). The first-stage model of our system was trained on synthetic data to explicitly extract blur and text size features. The second-stage model was trained to assess the quality of optical character recognition (OCR) based on the extracted features. The proposed system was tested on two publicly available datasets: SmartDoc-QA and SOC. The discrepancies in the results between our system and current state-of-the art methods are within statistical error. At the same time, our results are balanced for both datasets in terms of Pearson and Spearman Correlation Coefficients. In the proposed approach, features are extracted from image patches taken at different scales, thus making the system more stable and tolerant of variations in text size. Additionally, our approach results in a flexible and scalable solution that allows a trade-off between accuracy and speed. The source code is publicly available on github: https://github.com/RodinDmitry/QA-Two-Step.