Abstract
To predict the OCR accuracy of document images, text related image quality assessment is necessary and of great value, especially in online business processes. Such quality assessment is more interested in text and aims to compute the quality score of an image through predicting the degree of degradation at textual regions. In this paper, we propose a deep based framework to achieve image text quality assessment, which is composed of three stages: text detection, text quality prediction, and weighted pooling. Text detection is used to find potential text lines and the quality is solely estimated on detected text lines. To predict text line quality, we train a deep neural network model with our synthetic samples. The overall text quality of an image can be computed through pooling the quality of all detected text lines by way of weighted averaging. The proposed method has been tested on two benchmarks and our collected pictures. Experimental results show that the proposed method is feasible and promising in image text quality assessment.
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Li, H., Zhu, F., Qiu, J. (2018). DeepITQA: Deep Based Image Text Quality Assessment. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_34
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DOI: https://doi.org/10.1007/978-3-030-04224-0_34
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