Tuğrul TAŞCI, Belaynesh CHEKOL, Numan ÇELEBİ

Segmented character recognition using curvature-based global image feature

Character recognition in natural scene images is a fundamental prerequisite for many text-based imageanalysis tasks. Generally, local image features are employed widely to recognize characters segmented from natural sceneimages. In this paper, a curvature-based global image feature and description for segmented character recognition isproposed. This feature is entirely dependent on the curvature information of the image pixels. The proposed feature isemployed for segmented character recognition using Chars74k dataset and ICDAR 2003 character recognition dataset.From the two datasets, 1068 and 540 images of characters, respectively, are randomly chosen and 573-dimensionalfeature vector is synthesized per image. Quadratic, linear and cubic support vector machines are trained to examinethe performance of the proposed feature. The proposed global feature and two well-known local feature descriptorscalled scale invariant feature transform (SIFT) and histogram of oriented gradients (HOG) are compared in terms ofclassification accuracy, computation time, classifier prediction and training time. Experimental results indicate that theproposed feature yielded higher classification accuracy (%65.3) than SIFT (%53), performed better than HOG and SIFTin terms of classifier training time, and achieved better prediction speed than HOG and less computational time thanSIFT.

PDF

___

[1] Huang X, Shen T, Wang R, Gao C. Text detection and recognition in natural scene images. In: IEEE 2015 International Conference on Estimation, Detection and Information Fusion; Harbin, China; 2015. pp. 44-49.
[2] Kim J, Yoon HS. Graph matching method for character recognition in natural scene images: A study of character recognition in natural scene image considering visual impairments. In: IEEE 2011 International Conference on Intelligent Engineering Systems; Poprad, Slovakia; 2011. pp. 347-350.
[3] Elagouni K, Garcia C, Mamalet F, Sebillot P. Combining multi-scale character recognition and linguistic knowledge for natural scene text OCR. In: IEEE 2012 International Workshop on Document Analysis Systems; Gold Cost, QLD, Australia; 2012. pp. 120-124.
[4] Su B, Lu S, Tian S, Lim JH, Tan CL. Character recognition in natural scenes using convolutional co-occurrence hog. In: IEEE 2014 International Conference on Pattern Recognition; Stockholm, Sweden; 2014. pp. 2926-2931.
[5] Wang K, Babenko B, Belongie S. End-to-end scene text recognition. In: IEEE 2011 International Conference on Computer Vision; Barcelona, Spain; 2011. pp. 1457-1464.
[6] De Campos TE, Babu BR, Varma M. Character recognition in natural images. In: VISAPP 2009 Proceedings of the 4th International Conference on Computer Vision Theory and Applications; Lisboa, Portugal; 2009. pp. 273-280.
[7] Lucas SM, Panaretos A, Sosa L, Tang A, Wong S et al. ICDAR 2003 robust reading competitions. In: IEEE 2003 International Conference on Document Analysis and Recognition; Colchester, UK; 2003. pp. 682-687.
[8] Wang Y, Shi C, Wang C, Xiao B, Qi C. Multi-order co-occurrence activations encoded with Fisher Vector for scene character recognition. Pattern Recognition Letters 2017; 97: 69-76. doi: 10.1016/j.patrec.2017.07.011
[9] Milyaev S, Barinova O, Novikova T, Kohli P, Lempitsky V. Image binarization for end-to-end text understanding in natural images. In: IEEE 2013 International Conference on Document Analysis and Recognition; Washington, DC, USA; 2013. pp. 128-132.
[10] Mishra A, Alahari K, Jawahar CV. Top-down and bottom-up cues for scene text recognition. In: IEEE 2012 Conference on Computer Vision and Pattern Recognition; Providence, RI, USA; 2012. pp. 2687-2694.
[11] Shi C, Wang Y, Jia F, He K, Wang C et al. Fisher vector for scene character recognition: A comprehensive evaluation. Pattern Recognition 2017; 72: 1-14. doi: 10.1016/j.patcog.2017.06.022
[12] Sheshadri K, Diwala SK. Exemplar driven character recognition in the wild. In: BMVC 2012 British Machine Vision Conference; Surrey, UK; 2012. pp. 1-10.
[13] Neumann L, Matas J. A method for text localization and recognition in real-world images. In: Kimmel R, Klette R, Sugimoto A (editor). Computer Vision – ACCV 2010. Part III. Berlin, Heidelberg: Springer, 2010. pp. 770-783.
[14] Yi C, Yang X, Tian Y. Feature representations for scene text character recognition: A comparative study. In: IEEE 2013 International Conference on Document Analysis and Recognition; Washington, DC, USA; 2013. pp. 907-911.
[15] Sinha Y, Jain P, Kasliwal N. Comparative study of pre-processing and classification methods in character recognition of natural scene images. In: Singh R, Vatsa M, Majumdar A, Kumar A (editors). Machine Intelligence and Signal Processing: Advances in Intelligent Systems and Computing. Vol 390. New Delhi, India: Springer, 2016. pp. 119-129.
[16] Yang CS, Yang YH. Improved local binary pattern for real scene optical character recognition. Pattern Recognition Letters 2017; 100:14-21. doi: 10.1016/j.patrec.2017.08.005
[17] Coates A, Carpenter B, Case C, Satheesh S, Suresh B et al. Text detection and character recognition in scene images with unsupervised feature learning. In: IEEE 2011 International Conference on Document Analysis and Recognition; Beijing, China; 2011. pp. 440-445.
[18] Liu X, Lu T. Natural scene character recognition using Markov random field. In: IEEE 2015 International Conference on Document Analysis and Recognition; Tunis, Tunisia; 2015. pp. 396-400.
[19] Higa K, Hotta S. Local subspace classifier with transformation invariance for appearance-based character recognition in natural images. In: IEEE 2013 International Conference on Document Analysis and Recognition; Washington, DC, USA; 2013. pp. 533-537.
[20] Tian S, Bhattacharya U, Lu S, Su B, Wang Q et al. Multilingual scene character recognition with co-occurrence of histogram of oriented gradients. Pattern Recognition 2016; 51:125-134. doi:10.1016/j.patcog.2015.07.009
[21] Zhu Y, Sun J, Naoi S. Recognizing natural scene characters by convolutional neural network and bimodal image enhancement. In: Iwamura M, Shafait F (editors). Camera-Based Document Analysis and Recognition. Vol. 7139. Berlin, Heidelberg: Springer, 2011, pp. 69-82.
[22] Mohideen F, Rodrigo R. Curvature based robust descriptors. In: BMVC 2012 British Machine Vision Conference; Surrey, UK; 2012. pp. 1-11.
[23] Dalle D. Comparison of numerical techniques for Euclidean curvature. Rose-Hulman Undergraduate Mathematics Journal 2006; 7(1): 12.
[24] Lowe DG. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 2004; 60 (2): 91-110. doi: 10.1023/B:VISI.0000029664.99615.94.
[25] Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: IEEE 2005 Computer Society International Conference on Computer Vision; San Diego, USA; 2005. pp. 886-893.
[26] Cortes C, Vapnik V. Support-vector networks. Machine Learning 1995; 20 (3): 273-297. doi:10.1007/BF00994018
[27] Suykens JA, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters 1999; 9 (3): 293-300. doi:10.1023/A:1018628609742