Enhance Text Recognition by Image Pre-Processing to Facilitate Library Services by Mobile Devices

Chuen-Min Huang, Yi-Ling Chuang, Rih-Wei Chang, Ya-Yun Chen


Facing the popularity of web searching, libraries continuously invest in the provision of online searching and refurnish physical facilities to attract users during the past decades. In this study, we conducted a technical feasibility study to facilitate library services by applying a novel image pre-processing technique to enhance performance of OCR via mobile devices. In the binarization stage, a grayscale image is usually assigned a global threshold value to be binary, while this will not be suitable for some scenarios, such as non-uniform lightness and complicated background. Instead of segregating the grayscale image into many regions like other studies, our approach only partitioned an image into three equal-sized horizontal segments to identify the local threshold value of each segment and then restored the three segments back to the original state. The experimental results illustrate that the proposed method efficiently and effectively improves the text recognition. The accuracy rate was raised from 17.7% to 72.05% of all test images. Without counting eight unrecognizable images, the average accuracy rates of our treatment can reach 90.06%. To compare with other studies we conducted another evaluation to examine the validity of our approach. The result showed that our treatment outperforms most of the other studies and the performance achieves 74.6% in precision and 80.2% in the recall.We are confident that this design will not only bring users more convenience in using libraries but help library staff and businessmen to manage the status of books.


  1. Besag, J. 1989. Digital Image Processing: Toward Bayesian Image Analysis. Journal Of Applied Statistics, 16, 395-407.
  2. Bieniecki, W., Grabowski, S. & Rozenberg, W. 2007. Image Preprocessing For Improving Ocr Accuracy. International Conference On Perspective Technologies And Methods In Mems Design.
  3. Chowdhury, S. P., Dhar, S., Das, A. K., Chanda, B. & Mcmenemy, K. Robust Extraction Of Text From Camera Images. Proceedings Of The 10th International Conference On Document Analysis And Recognition, 2009 Barcelona, Spain. 1635445: Ieee Computer Society, 1280-1284.
  4. Foster, C. 1995. Pdas And The Library Without A Roof. Journal Of Computing In Higher Education, 7, 85-93.
  5. Holley, R. 2009. How Good Can It Get? Analysing And Improving Ocr Accuracy In Large Scale Historic Newspaper Digitisation Programs. D-Lib Magazine.
  6. Jian, Y., Yi, Z., Kok-Kiong, T. & Tong-Heng, L. Text Extraction From Images Captured Via Mobile And Digital Devices. Proceedings Of The Ieee/Asme International Conference On Advanced Intelligent Mechatronics(Aim), 14-17 July 2009. 566-571.
  7. Kaur, A. 2013. Mingle Face Detection Using Adaptive Thresholding And Hybrid Median Filter. International Journal Of Computer Applications In Technology, 70, 13-17.
  8. Kim, E., Lee, S. & Kim, J. 2009. Scene Text Extraction Using Focus Of Mobile Camera. The 10th International Conference On Document Analysis And Recognition.
  9. Kim, K. C., Byun, H. R., Song, Y. J., Choi, Y. W., Chi, S. Y., Kim, K. K. & Chung, Y. K. Scene Text Extraction In Natural Scene Images Using Hierarchical Feature Combining And Verification. Proceedings Of The 17th International Conference On Pattern Recognition(Icpr) 23-26 Aug 2004. 679-682.
  10. Lawler, R. 2013. Mary Meeker's 2013 Internet Trends: Mobile Makes Up 15% Of All Internet Traffic, With 1.5b Users Worldwide [Online]. Available: Http://Techcrunch.Com/2013/05/29/Mary-Meeker2013-Internet-Trends/.
  11. Liu, X. & Samarabandu, J. Multiscale Edge-Based Text Extraction From Complex Images. Proceedings Of The Ieee International Conference On Multimedia And Expo, 9-12 July 2006 Toronto,Ontario,Canada. 1721- 1724.
  12. Marqués, F. & Vilaplana, V. 2002. Face Segmentation And Tracking Based On Connected Operators And Partition Projection. Pattern Recognition, 35, 601-614.
  13. Mills, K. 2010. M-Libraries: Information Use On The Move. In: Neeham, G. & Ally, M. (Eds.) M-Libraries 2: A Virtual Library In Everyone's Pocket. London: Facet Publishi.
  14. Minetto, R., Thome, N., Cord, M., Stolfi, J., Precioso, F., Guyomard, J. & Leite, N. J. 2011. Text Detection And Recognition In Urban Scenes. Ieee International Conference On Computer Vision Workshops (Iccv Workshops).
  15. Mori, S., Suen, C. Y. & Yamamoto, K. 1992. Historical Review Of Ocr Research And Development. Proceedings Of The Ieee, 80, 1029-1058.
  16. Pei-Jun, L. & Effendi 2010. Adaptive Edge-Oriented Depth Image Smoothing Approach For Depth Image Based Rendering. Ieee International Symposium On Broadband Multimedia Systems And Broadcasting (Bmsb).
  17. Raza, M. U., Ullah, A., Ghori, K. M. & Haider, S. Text Extraction Using Artificial Neural Networks. Proceedings Of The International Conference On Networked Computing And Advanced Information Management, June 2001 Gyeongju, GyeongsangbukDo, South Korea. 134-137.
  18. Rong-Yuh, H. The Design And Implementation Of Mobile Navigation System For The Digital Libraries. Proceedings Of The Sixth International Conference On Information Visualisation 2002. 65-69.
  19. Shuqing, Z. & Qiaoning, Y. Microarray Images Processing Based On Mathematical Morphology. Proceedings Of The 2006 8th International Conference On Signal Processing, 16-20 2006 2006.
  20. Shutao, L. & Kwok, J. T. Text Extraction Using Edge Detection And Morphological Dilation. Proceedings Of The International Symposium On Intelligent Multimedia, Video And Speech Processing, 20-22 Oct 2004. 330-333.
  21. Taisheng, L., Xuan, Z. & Chongrong, L. 2012. An Improved Adaptive Image Filter For Edge And Detail Information Preservation. International Conference On Systems And Informatics (Icsai).
  22. Wai-Lin, C. & Chi-Man, P. Robust Character Recognition Using Connected-Component Extraction. Proceedings Of The Seventh International Conference On Intelligent Information Hiding And Multimedia Signal Processing (Iih-Msp), 14-16 Oct 2011. 310- 313.
  23. Ye, Q., Huang, Q., Gao, W. & Zhao, D. 2005. Fast And Robust Text Detection In Images And Video Frames. Image Vision Comput, 23, 565-576.

Paper Citation

in Harvard Style

Huang C., Chuang Y., Chang R. and Chen Y. (2014). Enhance Text Recognition by Image Pre-Processing to Facilitate Library Services by Mobile Devices . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 453-460. DOI: 10.5220/0004818504530460

in Bibtex Style

author={Chuen-Min Huang and Yi-Ling Chuang and Rih-Wei Chang and Ya-Yun Chen},
title={Enhance Text Recognition by Image Pre-Processing to Facilitate Library Services by Mobile Devices},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},

in EndNote Style

JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Enhance Text Recognition by Image Pre-Processing to Facilitate Library Services by Mobile Devices
SN - 978-989-758-015-4
AU - Huang C.
AU - Chuang Y.
AU - Chang R.
AU - Chen Y.
PY - 2014
SP - 453
EP - 460
DO - 10.5220/0004818504530460