Abstract
The quality of the image is critical to Optical Character Recognition (OCR), poor quality images will lead OCR to generate unreliable results. There are relative high ratio of low quality images in practical OCR-based application scenarios, how to evaluate quality of image and filter out unqualified images by document image quality assessment (DIQA) algorithms effectively is a big challenge for these scenarios. Current DIQA algorithms mainly focus on the overall image features rather than the text region, while the quality of the text region is dominant factor for OCR. In this paper, we propose a document image quality assessment algorithm based on information entropy in text region of image. Our algorithmic framework mainly consists of three networks to detect, extract and evaluate text region in image respectively. We build a quality prediction network based on HyperNet, and use the information entropy of the text region as the score weight, so that the final score can reflect the quality of the text region better. Finally, testing results on benchmark dataset SmartDoc-QA and our constructed dataset DocImage1k demonstrate that the proposed algorithm achieves excellent performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kong, T., Yao, A., Chen, Y., Sun, F.: Hypernet: Towards accurate region proposal generation and joint object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 845–853 (2016)
Nayef, N., Luqman, M. M., Prum, S., Eskenazi, S., Chazalon, J., Ogier, J. M.: SmartDoc-QA: a dataset for quality assessment of smartphone captured document images-single and multiple distortions. In: 2015 International Conference on Document Analysis and Recognition (ICDAR), pp. 1231–1235 (2015)
Blando, L. R., Kanai, J., Nartker, T. A.: Prediction of OCR accuracy using simple image features. In: 1995 International Conference on Document Analysis and Recognition, pp. 319–322 (1995)
Souza, A., Cheriet, M., Naoi, S., Suen, C. Y.: Automatic filter selection using image quality assessment. In: 2003 International Conference on Document Analysis and Recognition, pp. 508–512 (2003)
Cannon, M., Hochberg, J., Kelly, P.: Quality assessment and restoration of typewritten document images. Int. J. Doc. Anal. Recogn. 80–89 (1999)
Peng, X., Cao, H., Subramanian, K., Prasad, R.,Natarajan, P.: Automated image quality assessment for camera-captured OCR. In: 2011 IEEE International Conference on Image Processing, pp. 2621–2624 (2011)
Kumar, J., Chen, F., Doermann, D.: Sharpness estimation for document and scene images. In: 2012 International Conference on Pattern Recognition (ICPR), pp. 3292–3295 (2012)
Bui, Q. A., Molard, D., Tabbone, S.: Predicting mobile-captured document images sharpness quality. In: 2018 International Workshop on Document Analysis Systems (DAS), pp. 275–280 (2018)
Li, H., Zhu, F., Qiu, J.: Towards document image quality assessment: a text line based framework and a synthetic text line image dataset. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 551–558 (2019)
Peng, X., Wang, C.: Camera captured DIQA with linearity and monotonicity constraints. In: 2020 International Workshop on Document Analysis Systems, pp. 168–181 (2020)
Lu, T., Dooms, A.: A deep transfer learning approach to document image quality assessment. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1372–1377 (2019)
Rodin, D., Loginov, V., Zagaynov, I., Orlov, N.: Document image quality assessment via explicit blur and text size estimation. In: 2021 International Conference on Document Analysis and Recognition(ICDAR), pp. 281–292 (2021)
Liao, M., Wan, Z., Yao, C., Chen, K., Bai, X.: Real-time scene text detection with differentiable binarization. In: 2020 Conference on Artificial Intelligence, pp. 11474–11481 (2020)
Kim, J., Zeng, H., Ghadiyaram, D., Lee, S., Zhang, L., Bovik, A.C.: Deep convolutional neural models for picture-quality prediction: challenges and solutions to data-driven image quality assessment. IEEE Signal Process. Mag. 34(6), 130–141 (2017)
Ye, P., Kumar, J., Kang, L., Doermann, D.: Unsupervised feature learning framework for no-reference image quality assessment. In: 2012 Conference on Computer Vision and Pattern Recognition(CVPR), pp. 1098–1105 (2012)
Nayef, N., Ogier, J. M.: Metric-based no-reference quality assessment of heterogeneous document images. In: Document Recognition and Retrieval XXII, vol. 9402, p. 94020L (2015)
Li, H., Zhu, F., Qiu, J.:CG-DIQA: no-reference document image quality assessment based on character gradient. In: 2018 International Conference on Pattern Recognition (ICPR), pp. 3622–3626 (2018)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Z., Qiu, J., He, H. (2023). A Document Image Quality Assessment Algorithm Based on Information Entropy in Text Region. In: Xiong, N., Li, M., Li, K., Xiao, Z., Liao, L., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-031-20738-9_72
Download citation
DOI: https://doi.org/10.1007/978-3-031-20738-9_72
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20737-2
Online ISBN: 978-3-031-20738-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)