Abstract
One of the key difficulties of computer vision is the localization and retrieval of words or phrases from natural scene images. It is a technique that is used to recognize and isolate the desired text from the images. There are researchers who explored this field and concluded with good results, they mainly concentrated on the English language, but there is a need to work on local/regional languages, Country like India is having a vast portion of the rural area so there is a need to highlight the different Indian languages and also addresses the official scripts and their Unicode ranges. In this paper, various detection or recognition approaches are highlighted especially for the scene images containing South Indian languages.
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References
Huang R, Xu B (2019) Text attention and focal negative loss for scene text detection. Int Joint Conf Neural Netw (IJCNN) 2019:1–8. https://doi.org/10.1109/IJCNN.2019.8851959
Boaz TK, Prabhakar CJ (2013) A novel approach for detection and localization of caption in video based on pixel pairs. In: National conference on challenges in research & technology in the coming decades (CRT 2013), pp 1–6. https://doi.org/10.1049/cp.2013.2488
Soni R, Kumar B, Chand S (2019) Text detection and localization in natural scene images based on text awareness score. Appl Intell 49:1376–1405. https://doi.org/10.1007/s10489-018-1338-4
Zhu Y, Yao C, Bai X. Scene text detection and recognition: recent advances and future trends. https://doi.org/10.1007/s11704-015-4488-0
Sain A, Bhunia AK, Roy PP, Pal U. Multi-oriented text detection and verification in video frames and scene images. https://doi.org/10.1016/j.neucom.2017.09.089
http://u-pat.org/ICDAR2017/keynotes/ICDAR2017_Keynote_Prof_Bai.pdf
Multi-script robust reading competition ICDAR 2013. http://mile.ee.iisc.ernet.in/mrrc/index.html
Ding K, Liu Z, Jin L, Zhu X (2007) A comparative study of GABOR feature and gradient feature for handwritten Chinese character recognition. In: International conference on wavelet analysis and pattern recognition, Beijing, China, 2–4 Nov 2007, pp 1182–1186
Basavaraju HT, Aradhya VNM, Pavithra MS et al (2020) Arbitrary oriented multilingual text detection and segmentation using level set and Gaussian mixture model. Evol Intel. https://doi.org/10.1007/s12065-020-00472-y
Tulsyan K, Srivastava N, Mondal A, Jawahar CV. A benchmark system for Indian language text recognition. https://doi.org/10.1007/978-3-030-57058-3_6
Natarajan P, MacRostie E, Decerbo M (2005) The BBN Byblos Hindi OCR system. DRR 2005
Mathew M, Singh AK, Jawahar CV (2016) Multilingual OCR for Indic scripts. DAS 2016
Mathew M, Jain M, Jawahar CV (2017) Benchmarking scene text recognition in Devanagari, Telugu and Malayalam. https://doi.org/10.1109/ICDAR.2017.364
Nag S, Ganguly PK, Roy S. Offline extraction of indic regional language from natural scene image using text segmentation and deep convolutional sequence. https://doi.org/10.1007/978-981-13-2345-4_5
Aradhya VNM, Pavithra MS, Naveena C (2012) A robust multilingual text detection approach based on transforms and wavelet entropy. Proc Technol 4:232–237
Pavithra MS, Aradhya VNM (2014) A comprehensive of transforms, Gabor filter and k-means clustering for text detection in images and video. Appl Comput Inform
Bosamiya JH, Agrawal P, Roy PP, Balasubramanian R (2015) Script independent scene text segmentation using fast stroke width transform and grab cut. In: 2015 3rd IAPR Asian conference on pattern recognition
Raghunandan KS, Shivakumara P, Roy S, Hemantha Kumar G, Pal U (2018) Multi-script-oriented text detection and recognition in video/scene/born digital images. IEEE Trans Circ Syst Video Technol. https://doi.org/10.1109/TCSVT.2018.2817642
Naveena C, Ajay BN, Manjunath Aradhya VN (2019) Transform-based text detection approach in images. In: Satapathy S, Bhateja V, Somanah R, Yang XS, Senkerik R (eds) Information systems design and intelligent applications. Advances in intelligent systems and computing, vol 863. Springer, Singapore. https://doi.org/10.1007/978-981-13-3338-5_40
https://cvit.iiit.ac.in/research/projects/cvit-projects/indic-hw-data
Levenshtein V (1966) Binary codes capable of correcting deletions, insertions and reversals. Soviet Phys Doklady
Klakow D, Peters J (2002) Testing the correlation of word error rate and perplexity. Speech Commun
Manjunath Aradhya VN, Pavithra MS (2016) A comprehensive of transforms, Gabor filter and k-means clustering for text detection in images and video. Appl Comput Inform 12(2):109–116. ISSN 2210-8327. https://doi.org/10.1016/j.aci.2014.08.001
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Atmakuri, V., Dhanalakshmi, M. (2022). A Review of Scene Text Detection and Recognition of South Indian Languages in Natural Scene Images. In: Kumar, A., Ghinea, G., Merugu, S., Hashimoto, T. (eds) Proceedings of the International Conference on Cognitive and Intelligent Computing. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-2350-0_14
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