Abstract
Online handwriting recognition (OHR) has gained major research interest not just due to the enormous technological advancement in recent years, but also the easy availability of the various electronic devices. This digital revolution is opening up a new dimension in every passing day to the regional and low resource languages with these languages get noticed by the researchers. In this paper, we have targeted a low resource language, Assamese, which is mainly spoken in the eastern region of India. We have proposed a novel and efficient feature vector for recognition of online handwritten Assamese numeral images. Our feature vector has been conceptualized based on the properties of light rays emerging out from a point source. Here we consider that there are multiple hypothetical light emerging sources in a sample numeral image. The amount of light fenced by the image is quantified and considered as a feature. The idea of using point light source to estimate the shape of online handwritten numerals is completely new and efficient. Impressive recognition accuracy is obtained on application of the feature vector on a standard online handwritten Assamese numeral database and it outnumbers some popular and standard feature descriptors, available in the literature. The source code of this work can be found in the following github link: https://github.com/ghoshsoulib/CTRL-Assamese-Digit-Recognition.









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References
Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466
Alam F, Mehmood R, Katib I, Altowaijri SM, Albeshri A (2019) TAAWUN: a decision fusion and feature specific road detection approach for connected autonomous vehicles. Mob Networks Appl 1–17
Alam F, Mehmood R, Katib I (2020) “Comparison of Decision Trees and Deep Learning for Object Classification in Autonomous Driving,” in Smart Infrastructure and Applications, Springer, Cham, pp. 135–158
Albregtsen F (2008) “Statistical Texture Measures Computed from Gray Level Coocurrence Matrices,” … Lab. Dep. Informatics, Univ. …, pp. 1–14
Alghazo JM, Latif G, Elhassan A, Alzubaidi L, Al-Hmouz A, Al-Hmouz R (2017) An online numeral recognition system using improved structural features – a unified method for handwritten Arabic and Persian numerals. J Telecommun Electron Comput Eng 9(2–10):33–40
Ali Abuzaraida M, Zeki AM, Zeki AM (2015) Online recognition system for handwritten arabic digits 45–49
Assamese Handwritten Digits | IEEE DataPort. [Online]. Available: https://ieee-dataport.org/documents/assamese-handwritten-digits. [Accessed: 30-Aug-2020]
Azeem SA, El Meseery M, Ahmed H (2012) Online Arabic handwritten digits recognition. Proc Int Work Front Handwri Recog, IWFHR 135–140
Bahlmann C, Burkhardt H (2004) The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Trans Pattern Anal Mach Intell 26(3):299–310
Baruah U, Hazarika SM (2015) A dataset of online handwritten Assamese characters. J Inf Process Syst 11(3):325–341
Bhattacharya U, Gupta BK, Parui SK (2007) Direction code based features for recognition of online handwritten characters of Bangla. Proc Int Conf Doc Anal Recog ICDAR 1:58–62
Cilia ND, De Stefano C, Fontanella F, Scotto di Freca A (2019) A ranking-based feature selection approach for handwritten character recognition. Pattern Recogn Lett 121:77–86
Das N, Basu S, Sarkar R, Kundu M, Nasipuri M, Kumar Basu D (2015) An Improved Feature Descriptor for Recognition of Handwritten Bangla Alphabet
Fan GF, Peng LL, Hong WC, Sun F (Jan. 2016) Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing 173:958–970
Ghosh S, Bhowmik S, Ghosh K, Sarkar R, Chakraborty S (2019) “A filter ensemble feature selection method for handwritten numeral recognition,” No. Aprir
Ghosh S, Bhattacharya R, Majhi S, Bhowmik S, Malakar S, Sarkar R (2019) Textual Content Retrieval from Filled-in Form Images. Comm Comput Inform Sci 1020:27–37
Ghosh S, Chatterjee A, Singh PK, Bhowmik S, Sarkar R (2020) Language-invariant novel feature descriptors for handwritten numeral recognition. Vis Comput, pp. 1–23
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software. ACM SIGKDD Explor Newsl 11(1):10
Jindal A, Aggarwal N, Gupta S (2018) An obstacle detection method for visually impaired persons by ground plane removal using speeded-up robust features and gray level co-occurrence matrix. Pattern Recognit Image Anal 28(2):288–300
Kapoor V, Gupta P (2013) Digit recognition system by using Back propagation algorithm. Int J Comput Appl 83(8):33–36
Kherallah M, Haddad L, Alimi AM, Mitiche A (2008) On-line handwritten digit recognition based on trajectory and velocity modeling. Pattern Recogn Lett 29(5):580–594
Xiaolin Li, Plamondon R, Parizeau M (2002) Model-based online handwritten digit recognition 1134–1136
“Machine Learning in Cognitive IoT - 1st Edition - Neeraj Kumar - Aais.” [Online]. Available: https://www.routledge.com/Machine-Learning-in-Cognitive-IoT/Kumar-Makkar/p/book/9780367359164. [Accessed: 30-Aug-2020]
Mandal A et al (2018) “A case study of genetic algorithm coupled multi-layer perceptron,” in International conference on emerging Technologies for Sustainable Development (ICETSD ‘19)
Medhi K, Kalita SK (2015) Assamese digit recognition with feed forward neural network. Int J Comput Appl 109(1):34–40
Musleh D, Halawani K, Mahmoud S (2017) Fuzzy modeling for handwritten Arabic numeral recognition. Int Arab J Inf Technol 14(4):502–511
Pal A et al (2016) “Online Bengali handwritten numerals recognition using Deep Autoencoders,” in 2016 22nd National Conference on communication, NCC 2016
Potrus MY, Ngah UK, Sakim HAM, AbdulRahman SA (2010) Normalization and rectification method for online Hindi digit recognition with partial alignment algorithm. ICEIE 2010–2010 Int Conf Electron Inform Eng Proc 1
Ramakrishnan AG Urala KB (2013) Global and local features for recognition of online handwritten numerals and Tamil characters. ACM Int Sonf Proc Ser
Razzak MI, Hussain SA, Sher M (2009) Numeral recognition for Urdu script in unconstrained environment. Int Conf Emerg Technol, ICET 2009:44–47
Reddy GS, Sarma B, Naik RK, Prasanna SRM, Mahanta C (2012) Assamese online handwritten digit recognition system using hidden Markov models. ACM Int Conf Proc Ser 108–113
Roy K (2012) Stroke-Database Design for Online Handwriting Recognition in Bangla 2(4): 2534–2540
Samanta R, Ghosh S, Chatterjee A, Sarkar R (2019) A Novel Approach Towards Handwritten Digit Recognition Using Refraction Property of Light Rays. Int J Comput Vis Image Process 10(3)
Sarma B, Mehrotra K, Krishna Naik R, Prasanna SRM, Belhe S, Mahanta C (2013) Handwritten Assamese numeral recognizer using HMM & SVM classifiers. Ntnl Conf Commun, NCC 2013:2013
Sen S, Mitra M, Chowdhury S, Sarkar R, Roy K (2016) Quad-tree based image segmentation and feature extraction to recognize online handwritten Bangla characters. Lect Notes Comput Sci (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) LNAI 9896:246–256
Sen S, Bhattacharyya A, Singh PK, Sarkar R, Roy K, Doermann D (2018) Application of structural and topological features to recognize online handwriten bangla characters. ACM Trans Asian Low-Resource Lang Inf Process 17(3):1–16
Shim J, Ansari MI (2017) Online digit recognition using start and end point. J Multimed Inf Syst 4(1):39–42
Singh H, Sharma RK, Kumar R, Verma K, Kumar R, Kumar M (2020) A benchmark dataset of online handwritten gurmukhi script words and numerals. Commun Comput Inform Sci (CCIS) 1148, 457–466
Tagougui N, Kherallah M, Alimi AM (2013) Online Arabic handwriting recognition: a survey. Int J Doc Anal Recognit 16(3):209–226
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Ghosh, S., Chatterjee, A., Sen, S. et al. CTRL –CapTuRedLight: a novel feature descriptor for online Assamese numeral recognition. Multimed Tools Appl 80, 30033–30056 (2021). https://doi.org/10.1007/s11042-020-10081-7
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DOI: https://doi.org/10.1007/s11042-020-10081-7