Abstract
A pioneer conceptual combination of level set method and Gaussian Mixture Model (GMM) is presented in the described multilingual, arbitrary-oriented character segmentation. The method is a serial accomplishment processes of Gaussian low pass filter, single level 2 Dimensional Discrete Wavelet Transform (2D DWT) for better feature extraction and implemented level set method, k-means clustering algorithm with GMM to achieve a veracious character segmentation results by detecting great measure of true text region of an image. The proposed method segments a character chiefly by distinguishing the touching character constituents and also deals discontinuities presence in a character by using Laplacian of Gaussian filter and morphological bridge function in a intellectual way. The exhibited method was explored on Multi-script Robust Reading Competition dataset and on our privately collected graphical and handwritten multilingual, arbitrarily-oriented text images. The suggested method is compared with the well known multilingual and arbitrarily-oriented charter segmentation methods. The described method attains better segmentation outcomes when compared to the familiar functioning methods. Hence, the suggested method is highly suitable to consider as an improved, standard and procedural technique.
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Abi-Haidar A, Rocha LM (2011) Collective classification of textual documents by guided self-organization in T-cell cross-regulation dynamics. Evol Intell 4(2):69–80
Aradhya VNM, Kumar GH, Noushath S (2008) Multilingual OCR system for south Indian scripts and English documents: an approach based on fourier transform and principal component analysis. Eng Appl Artif Intell 21(4):658–668
Manjunath Aradhya VN, Basavaraju HT, Guru DS (2019) Decade research on text detection in images/videos: a review. Evol Intel. https://doi.org/10.1007/s12065-019-00248-z
Rada L, Chen K (2013) On a variational model for selective image segmentation of features with infinite perimeter. J Math Res Appl 33(3):253–272
Ghoshal R, Banerjee A (2020) SVM and MLP based segmentation and recognition of text from scene images through an effective binarization scheme. Comput Intel Pattern Recognit 999:237–246
Chirvonaya AN, Sheshkus AV, Arlazarov VL (2020) Monospaced font detection using character segmentation and Fourier transform. In: 12th International Conference on Machine Vision 11433:1143317
Bhateja V, Devi S, Urooj S (2013) An evaluation of edge detection algorithms for mammographic calcifications. In: Proceedings of the 4th international conference on signal and image processing, pp 487–498
Moin A, Bhateja V, Srivastava A (2016) Weighted-PCA based multimodal medical image fusion in contourlet domain. Proceedings of the international congress on information and communication technology pp 597–605
Srivastava A, Bhateja V, Moin A (2017) Combination of PCA and contourlets for multispectral image fusion. In: Proceedings of the international conference on data engineering and communication technology, pp 577–585
Hebbi C, Mamatha HR, Sahana YS, Dhage S, Somayaji S (2020) A convolution neural networks based character and word recognition system for similar script languages Kannada and Telugu. In: Proceedings of ICETIT, pp 306–317
Khanderao MS, Ruikar S (2020) Character segmentation and recognition of Indian Devanagari script. In: ICT analysis and applications, pp 529–537
Villamizar M, Canévet O, Odobez JM (2020) Multi-scale sequential network for semantic text segmentation and localization. Pattern Recognit Lett 129:63–69
Rong X, Yi C, Tian Y (2019) Unambiguous scene text segmentation with referring expression comprehension. IEEE Trans Image Process 29:591–601
Nomura S, Yamanaka K, Katai O, Kawakami H, Shiose T (2005) A novel adaptive morphological approach for degraded character image segmentation. Pattern Recognit 38(11):1961–1975
Roy PP, Pal U, Lladós J, Delalandre M (2012) Multi-oriented touching text character segmentation in graphical documents using dynamic programming. Pattern Recognit 45(5):1972–1983
Shivakumara P, Bhowmick S, Su B, Tan CL, Pal U (2011) A new gradient based character segmentation method for video text recognition. In: 2011 International conference on document analysis and recognition, pp 126–130
Sharma N, Shivakumara P, Pal U, Blumenstein M, Tan CL (2013) A new method for character segmentation from multi-oriented video words. In: 2013 12th International conference on document analysis and recognition, pp 413-417
Palrecha N, Rai A, Kumar A, Srivastava S, Tyagi V (2011) Character segmentation for multi lingual Indic and Roman scripts. In: 2011 IEEE 7th international colloquium on signal processing and it’s applications, pp 45–49
Zoizou A, Zarghili A, Chaker I (2018) A new hybrid method for Arabic Multi-font text segmentation, and a reference corpus construction. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2018.07.003
Kavitha AS, Shivakumara P, Kumar GH, Lu T (2017) A new watershed model based system for character segmentation in degraded text lines. AEU Int J Electr Commun 71:45–52
Basavaraju HT, Aradhya VM, Guru DS (2019) Text detection through hidden Markov random field and EM-algorithm. In: Information systems design and intelligent applications, pp 19–29
Cheragui MA, Hiri E (2020) Arabic Text Segmentation using Contextual Exploration and Morphological Analysis. In: 2020 2nd International conference on mathematics and information technology (ICMIT), pp 220–225
Khan T, Mollah AF (2020) Text non-text classification based on area occupancy of equidistant pixels. Procedia Comput Sci 167:1889–1900
Singh A, Sarkhel R, Das N, Kundu M, Nasipuri M (2020) A skip-connected multi-column network for isolated handwritten bangla character and digit recognition. arXiv preprint arXiv:2004.12769
Aradhya VM, Pavithra MS, Naveena C (2012) A robust multilingual text detection approach based on transforms and wavelet entropy. Procedia Technol 4:232–237
Aradhya VM, Pavithra MS, Niranjan SK (2014) An exploration of wavelet transform and level set method for text detection in images and video frames. In: Recent advances in intelligent informatics, pp 419–426
Aradhya VNM, Pavithra MS (2014) An application of LBF energy in image/video frame text detection. In: 14th International conference on frontiers in handwriting recognition, pp 760–765
Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79(1):12–49
Zhang K, Zhang L, Song H, Zhou W (2010) Active contours with selective local or global segmentation: a new formulation and level set method. Image Vis Comput 28(4):668–676
Aradhya VNM, Pavithra MS (2013) An application of k-means clustering for improving video text detection. In: Intelligent informatics, pp 41–47
Pavithra MS, Aradhya VNM (2014) A comprehensive of transforms, Gabor filter and k-means clustering for text detection in images and video. In: Applied computing and informatics, pp 1–15
Reynolds DA (2009) Gaussian mixture models. In: Encyclopedia of biometrics, p 741
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Methodol) 39(1):1–22
Basavaraju HT, Aradhya VM, Guru DS (2018) A novel arbitrary-oriented multilingual text detection in images/video. In: Information and decision sciences, pp 519–529
Multi-script robust reading competition ICDAR 2013. http://mile.ee.iisc.ernet.in/mrrc/index.html
Phan TQ, Shivakumara P, Su B, Tan CL (2011) A gradient vector flow-based method for video character segmentation. In: 2011 International conference on document analysis and recognition, pp 1024–1028
ICDAR (2013) http://www.icdar2013.org/
Karatzas D, Mestre SR, Mas J, Nourbakhsh F, Roy PP (2011) ICDAR 2011 robust reading competition-challenge 1: reading text in born-digital images (web and email). In: 2011 International conference on document analysis and recognition, pp 1485–1490
Kasar T, Kumar D, Anil Prasad MN, Girish D, Ramakrishnan AG (2011) MAST: multi-script annotation toolkit for scenic text. In: Proceedings of the 2011 joint workshop on multilingual OCR and analytics for noisy unstructured text data, pp 1–8
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Basavaraju, H.T., Aradhya, V.N.M., Pavithra, M.S. et al. Arbitrary oriented multilingual text detection and segmentation using level set and Gaussian mixture model. Evol. Intel. 14, 881–894 (2021). https://doi.org/10.1007/s12065-020-00472-y
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DOI: https://doi.org/10.1007/s12065-020-00472-y