Abstract: A novel effective scheme for automated text detection and character recognition in natural scene images is presented in the paper. The proposed text detection approach belongs to the category of connected component-based methods utilizing Maximally Stable Extremal Regions (MSER) feature detector. Various literature based geometrical and contour oriented filters, used to distinguish between text and non-text MSER regions as well as to group remaining text regions into words and phrases, are applied first. Novel filters, designed to reject remaining non-text regions and words (phrases) that are not in line with assumed properties, are utilized next. Final words and phrases are recognized using an OCR system. Finally, an application of the presented approach within the IMCOP content discovery and delivery platform is briefly described.
B I B L I O G R A F I A1. Worring, M., Snoek, C.: Visual Content Analysis. Encyclopedia of Database Systems, pp. 3360–3365. Springer, Boston (2009)
2. Baran, R., Dziech, A., Zeja, A.: A capable multimedia content discovery platform based on visual content analysis and intelligent data enrichment. J. Multimedia Tools Appl. (2017)
3. Dziech, W., Baran, R., Wiraszka, D.: Signal compression based on zonal selection methods. In: Proceedings of International Conference on Mathematical Methods in Electromagnetic Theory, pp. 224–227 (2000)
4. Grega, M.: Enhanced method of near duplicate detection for red carpet photographs. In: Dziech, A., Leszczuk, M., Baran, R. (eds.) MCSS 2015. CCIS, vol. 566, pp. 132–140. Springer, Heidelberg (2015)
5. Baran, R., Rudziński, F., Zeja, A.: Face recognition for movie character and actor discrimination based on similarity scores. In: 2016 International Conference on Computational Science and Computational Intelligence, Las Vegas, pp. 1333–1338 (2016)
6. Jain, A.K., Bhattacharjee, S.: Text segmentation using gabor filters for automatic document processing. Mach. Vis. Appl. 5(3), 169–184 (1992)
7. Coates, A., Carpenter, B., Case, C., Satheesh, S., Suresh, B., Wang, T., Wu, D.J., Ng, A.J.: Text detection and character recognition in scene images with unsupervised feature learning. In: Proceedings of the 2011 International Conference on Document Analysis and Recognition (ICDAR 2011), pp. 440–445. IEEE Computer Society, Washington, DC (2011)
8. Ohya, J., Shio, A., Akamatsu, S.: Recognizing characters in scene images. IEEE Trans. Pattern Anal. Mach. Intell. 16(2), 214–220 (1994)
9. Neumann, L., Matas, J.: A Method for text localization and recognition in real-world images. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) Proceedings of the 10th Asian Conference on Computer Vision, vol. Part III, pp. 770–783. Springer, Heidelberg (2010)
10. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)
11. Merino-Gracia, C., Lenc, K., Mirmehdi. M.: A head-mounted device for recognizing text in natural scenes. In: Iwamura, M., Shafait, F. (eds.) Proceedings of the 4th International Conference on Camera-Based Document Analysis and Recognition, pp. 29–41. Springer, Heidelberg (2011)
12. Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R.: ICDAR 2003 robust reading competitions. In: Proceedings of the 7th International Conference on Document Analysis and Recognition, pp. 682–687 (2003)
13. Chen, S., Tsai, S., Schroth, G., Chen, D.M., Grzeszczuk, R., Girod, B.: Robust text detection in natural images with edge-enhanced Maximally Stable Extremal Regions. In: 2011 18th IEEE International Conference on Image Processing, Brussels, pp. 2609–2612 (2011)
14. OpenCV library. https://docs.opencv.org/3.3.0/d4/d61/group__text.html