A Comparative Study on OCR using Super-Resolution for Small Fonts

  • Cho, Wooyeong (Department of Electronics Engineering, Kwangwoon University) ;
  • Kwon, Juwon (Department of Electronics Engineering, Kwangwoon University) ;
  • Kwon, Soonchu (Graduate School of Smart Convergence, Kwangwoon University) ;
  • Yoo, Jisang (Department of Electronics Engineering, Kwangwoon University)
  • Received : 2019.07.16
  • Accepted : 2019.07.27
  • Published : 2019.09.30


Recently, there have been many issues related to text recognition using Tesseract. One of these issues is that the text recognition accuracy is significantly lower for smaller fonts. Tesseract extracts text by creating an outline with direction in the image. By searching the Tesseract database, template matching with characters with similar feature points is used to select the character with the lowest error. Because of the poor text extraction, the recognition accuracy is lowerd. In this paper, we compared text recognition accuracy after applying various super-resolution methods to smaller text images and experimented with how the recognition accuracy varies for various image size. In order to recognize small Korean text images, we have used super-resolution algorithms based on deep learning models such as SRCNN, ESRCNN, DSRCNN, and DCSCN. The dataset for training and testing consisted of Korean-based scanned images. The images was resized from 0.5 times to 0.8 times with 12pt font size. The experiment was performed on x0.5 resized images, and the experimental result showed that DCSCN super-resolution is the most efficient method to reduce precision error rate by 7.8%, and reduce the recall error rate by 8.4%. The experimental results have demonstrated that the accuracy of text recognition for smaller Korean fonts can be improved by adding super-resolution methods to the OCR preprocessing module.


Korean OCR;Tesseract;Super-resolution;Text-recognition;and Deep-learning


Supported by : Kwangwoon University


  1. S. Mori, C.Y. Suen, and K. Yamamoto, "Historical Review of OCR Research and Development," Proceedings of the IEEE, Vol. 80, No. 7, pp. 1029-1058, July 1992.
  2. I. Goodfellow, Y. Bengio, and A. Courville, Deep learning, MIT press, 2016.
  3. K. Greff, R.K. Srivastava, J. Koutnik, B.R. Steunebrink, and J. Schmidhuber, "LSTM: A search space odyssey." IEEE transactions on neural networks and learning systems 28, No. 10, July 2016.
  4. W. shi, J. Caballero, F. Huszar, J. Totz, A.P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, "Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874-1883, 2016.
  5. C. Dong, C.C. Loy, K. He, and X. Tang, "Learning a deep convolutional network for image super-resolution., " European conference on computer vision". Springer, Cham, 2014.
  6. C. Dong, C.C. Loy, and X. Tang, "Accelerating the super-resolution convolutional neural network.," European conference on computer vision. Springer, Cham, 2016.
  7. C. Dong, C.C. Loy, and X. Tang, "Image super-resolution using deep convolutional networks.," IEEE transactions on pattern analysis and machine intelligence 38., No. 2, pp.295-307, June 2015.
  8. L. faning, Z. Xiaoshu, and H. Chunjiao, "Single Image Super-Resolution Restoration Model Using Deep Convolutional Networks." Guangxi Sciences, pp.231-235, 2017.
  9. J. Yamanaka, S. Kuwashima, and T. Kurita, "Fast and accurate image super resolution by deep CNN with skip connection and network in network." International Conference on Neural Information Processing., pp. 217-225, Springer, Cham, November 2017.
  10. W. Sen, and Y. Kejian, "An image scaling algorithm based on bilinear interpolation with CV++" Journal of Techniques of Automation & Applications, pp.44-45, 2008.
  11. C. De Boor, "Bicubic spline interpolation." Journal of mathematics and physics 41." No. 1-4, pp. 212-218, Apr 1962 .
  12. M. Bevilacqua, A. Roumy, C. Guillemot, and M.L. Alberi-Morel, "Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding." British Machine Vision Conference, 2012.
  13. L. Xu, JS. Ren, C. Liu, J. Jia, "Deep convolutional neural network for image deconvolution.", Advances in neural information processing systems., pp. 1790-1798, 2014.
  14. K. He, X. Zhang, S. Ren, J. Sun, "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.