A REVIEW ON TEXT DETECTION TECHNIQUES
DOI:
https://doi.org/10.11113/jt.v78.8261Keywords:
Text Detection, Images, Videos, ICDARAbstract
Text detection in image is an important field. Reading text in image is challenging because of the variations in images. Text detection in images is useful for many navigational purposes e.g. text on google API’s and traffic panels etc. This paper analyzes the work done on text detection by many researchers and critically evaluates the techniques designed for text detection and states the limitation of each approach. We have integrated the work of many researchers for getting a brief over view of multiple available techniques and their strengths and limitations are also discussed to give readers a clear picture. The major dataset discussed in all these papers are ICDAR 2003, 2005, 2011, 2013 and SVT(street view text).References
Iqbal, K., Yin, X. C., Hao, H. W., Asghar, S., and Ali, H. 2014. K2 Algorithm-based Text Detection with An Adaptive Classifier Threshold. International Journal of Image Processing (IJIP), 8(3): 87-94.
Jaderberg, M., A. Vedaldi, and A. Zisserman 2014. Deep Features For Text Spotting. In Computer Vision–ECCV. 6 September 2014. 512-528.
HU, Q., L. R., HONG, and MA, L. L. 2013. Text Detection in Natural Scene Images. Computer Knowledge and Technology. 22: 45-47.
Huang, W., Lin, Z., Yang, J., & Wang, J. 2013. Text Localization In Natural Images Using Stroke Feature Transform And Text Covariance Descriptors. In Computer Vision (ICCV), 2013 IEEE International Conference. 1(2): 1241-1248.
Yao, C., X., Bai, B., Shi and W., Liu. 2014. Strokelets: A Learned Multi-Scale Representation For Scene Text Recognition. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference. 9 February 2014. 4042-4049.
Ye, Q., and D. Doermann. 2014. Scene Text Detection Via Integrated Discrimination Of Component Appearance And Consensus. In Camera-Based Document Analysis and Recognition 5th International Workshop, CBDAR 2013. Washington, DC, USA. 23 August 2013. 47-59.
Neumann, L., and J., Matas. 2013. Scene Text Localization And Recognition With Oriented Stroke Detection. In Computer Vision (ICCV), 2013 IEEE International Conference. 1(2):97-104.
Minetto, R., N., Thome, M., Cord, N. J., Leite, and J. Stolfi. 2014. Snoopertext: A Text Detection System For Automatic Indexing Of Urban Scenes. Computer Vision and Image Understanding. 122: 92-104.
Fabrizio, J., M., Cord, and B. Marcotegui. 2009. Text Extraction From Street Level Images. CMRT09-CityModels, Roads and Traffic. 38(3): 199-204.
Ganesh, V., and L. G. Malik. 2014. Extraction of Text from Images of Big Data. International Journal. 2(3): 40-46.
Gao, R., F., Shafait, S., Uchida, and Y., Feng. 2014. A Hierarchical Visual Saliency Model for Character Detection in Natural Scenes. In Camera-Based Document Analysis and Recognition. 23 August 2014. 18-29.
Walther, D., L., Itti, M., Riesenhuber, T., Poggio, and C. Koch. 2002. Attentional Selection For Object Recognition A Gentle Way. Biologically Motivated Computer Vision. 22 November 2002. 472-479.
Xiao, H., and Y. Rao. 2014. An Efficient Method of Text Localization in Complicated Background Scenes. Journal of Software. 9(6): 1538-1544.
Gonzalez, A., and L. M. Bergasa. 2013. A Text Reading Algorithm For Natural Images. Image and Vision Computing. 31(3): 255-274.
Gonzalez, A., L. M., Bergasa, and J. J. Yebes. 2014. Text Detection And Recognition On Traffic Panels From Street-Level Imagery Using Visual Appearance. Intelligent Transportation Systems, IEEE Transactions. Beijing, China. 6-11 July 2014. 15(1): 228-238.
Iqbal, K., X. C., Yin, H. W., Hao, S., Asghar, and H., Ali. 2014. Bayesian Network Scores Based Text Localization In Scene Images. In Neural Networks (IJCNN), 2014 International Joint Conference. Beijing, China. 6-11 July 2014. 15(1): 2218-2225.
Jaderberg, M., K., Simonyan, A., Vedaldi, and A., Zisserman. 2014. Synthetic Data And Artificial Neural Networks For Natural Scene Text Recognition. Arxiv Preprint Arxiv. 9 June 2014. 1406. 2227.
Yao, C., X., Bai, and W., Liu. 2014. A Unified Framework For Multi-Oriented Text Detection And Recognition. Image Processing, IEEE Transactions. 23(11): 4737-4749.
Milevskiy, I., and Y., Boykov. 2014. Joint Energy-based Detection and Classificationon of Multilingual Text Lines. arXiv preprint arXiv. 23 July 2014. 1407.6082.
Barlas, P., S., Adam, C., Chatelain and T. Paquet. 2014. A Typed And Handwritten Text Block Segmentation System for Analysis Systems (DAS). 11th IAPR International Workshop on heterogeneous and complex documents. Tours, France. 7-10 April 2014. 46 – 50.
Breuel, T. M. 2002. Two Geometric Algorithms For Layout Analysis. Document Analysis Systems v. 19 August 2002. 188-199.
Yin, X., X. C., Yin, H. W., Hao, and Iqbal, K. 2012. Effective Text Localization In Natural Scene Images With MSER, Geometry-Based Grouping And AdaBoost. In Pattern Recognition (ICPR), 2012 21st International Conference. Tsukuba, Japan. 11-15 November 2012. 725-728.
Hanif, S. M., and L. Prevost. 2009. Text Detection And Localization In Complex Scene Images Using Constrained Adaboost Algorithm. In Document Analysis and Recognition, 2009. ICDAR'09. 10th International Conference. Barcelona, Spain. 26-29 July 2009. 1-5
Ye, Q., J., Jiao, J., Huang and H. Yu. 2007. Text Detection And Restoration In Natural Scene Images. Journal of Visual Communication and Image Representation. 18(6): 504-513.
Shahab, A., F., Shafait, and A. Dengel. 2011. ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text In Scene Images. In Document Analysis and Recognition (ICDAR), 2011 International Conference. Beijing, China. 18-21 Sept. 2011. 1491-1496
Wolf, C., and J., M., Jolion. 2006. Object Count/Area Graphs For The Evaluation Of Object Detection And Segmentation Algorithms. International Journal of Document Analysis and Recognition (IJDAR). 8(4): 280-296.
Toyama, T., D., Sonntag, A., Dengel, T., Matsuda, M., Iwamura and K. Kise. 2014. A Mixed Reality Head-Mounted Text Translation System Using Eye Gaze Input. In Proceedings Of The 19th International Conference on Intelligent User Interfaces. Haifa, Israel. 24-27 February 2014. 329-334.
Kobayashi, T., T., Toyamaya, F., Shafait, M., Iwamura, K., Kise, And A., Dengel. 2012. Recognizing Words In Scenes With A Head-Mounted Eye-Tracker. In Document Analysis Systems (DAS), 2012 10th IAPR International Workshop. Gold Cost, QLD 27-29 March 2012. 333-338.
Wang, H. C., Y., Landa, M., Fallon, and S., Teller. 2014. Spatially Prioritized And Persistent Text Detection And Decoding. In Camera-Based Document Analysis and Recognition. 23 August 2014. 3-17
Shivakumara, P., Phan, T. Q., Lu, S., and Tan, C. L. 2013. Gradient Vector Flow And Grouping-Based Method For Arbitrarily Oriented Scene Text Detection In Video Images. Circuits and Systems for Video Technology, IEEE Transactions. 23(10): 1729-1739.
Wang, T., D. J., Wu, A., Coates, and A., Y., Ng. 2012. End-To-End Text Recognition With Convolutional Neural Networks. In Pattern Recognition (ICPR), 2012 21st International Conference. Tsukuba, Japan. 11-15 November 2012. 3304-3308.
Alsharif, O., and J. Pineau. 2013. End-To-End Text Recognition With Hybrid Hmm Maxout Models. arXiv preprint arXiv: 7 October 2013. 1310.1811.
Neumann, L., and J., Matas. 2012. Real-Time Scene Text Localization And Recognition. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference. Providence, RI. 16-21 June 2012. 3538-3545.
Wang, K., B., Babenko, and S. Belongie. 2011. End-to-end scene text recognition. In Computer Vision (ICCV), 2011 IEEE International Conference. Barcelona, Spain. 6-13 Nov. 2011. 1457-1464.
Singh, S., A., Gupta, and A. A. Efros. 2012. Unsupervised Discovery Of Mid-Level Discriminative Patches. Computer Vision–ECCV 2012. 3(4): 73-86.
Nomura, S., K., Yamanaka, O., Katai, H., Kawakami and T., Shiose. 2005. A Novel Adaptive Morphological Approach For Degraded Character Image Segmentation. Pattern Recognition. 38(11): 1961-1975
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
