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(searched for: doi:10.13176/11.485)
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, Dinesh V. Rojatkar
Lecture Notes in Electrical Engineering pp 131-138; https://doi.org/10.1007/978-981-15-1420-3_14

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, M. S. Shirdhonkar
International Journal of Information Technology, Volume 12, pp 1217-1226; https://doi.org/10.1007/s41870-019-00394-8

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Sk. Md. Obaidullah, , , Chayan Halder,
International Journal of Pattern Recognition and Artificial Intelligence, Volume 32; https://doi.org/10.1142/s0218001418560128

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Sk Md Obaidullah, , Chayan Halder, Nibaran Das,
International Journal of Machine Learning and Cybernetics, Volume 10, pp 87-106; https://doi.org/10.1007/s13042-017-0702-8

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, , Nibaran Das, Subhadip Basu, Mahantapas Kundu, Mita Nasipuri
Multimedia Tools and Applications, Volume 77, pp 8441-8473; https://doi.org/10.1007/s11042-017-4745-3

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, Supratim Das, Ram Sarkar, Mita Nasipuri
Advances in Intelligent Systems and Computing pp 787-795; https://doi.org/10.1007/978-981-10-3153-3_78

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Sk Md Obaidullah, Chitrita Goswami, , , Chayan Halder,
International Journal of Pattern Recognition and Artificial Intelligence, Volume 31; https://doi.org/10.1142/s0218001417530032

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, Chayan Halder, Nibaran Das,
Advances in Intelligent Systems and Computing pp 37-51; https://doi.org/10.1007/978-81-322-2653-6_3

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Advances in Intelligent Systems and Computing, Volume 379, pp 703-711; https://doi.org/10.1007/978-81-322-2517-1_67

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, Chayan Halder, Nibaran Das,
Published: 21 August 2015
Procedia Computer Science, Volume 54, pp 585-594; https://doi.org/10.1016/j.procs.2015.06.067

Abstract:
In this paper a novel HNSI (Handwritten Numeral Script Identification) framework to identify scripts from document images containing numeral text written by any one of the four popular Indic scripts namely Bangla, Devanagari, Roman and Urdu has been proposed. A dataset of 4000 word-level numeral images with equal distribution of each script type are collected from different individuals with varying age, sex and educational qualification. Some spatial and frequency domain features has been computed and a 55-dimensional feature vector is developed. During experimentation the whole dataset is divided into 2:1 ratio for training and testing. Performance of different classifiers is compared and MLP is found to be the best one while evaluating accuracy rate for combinations like Four-scripts, Tri-scripts and Bi-scripts.
2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS) pp 213-218; https://doi.org/10.1109/retis.2015.7232880

Abstract:
In a multi-script country like India, prior identification of script from document images is an essential step before choosing appropriate script specific OCR. The problem becomes more complex and challenging in case of HSI (Handwritten Script Identification). An automatic HSI technique for document images of six popular Indic scripts namely Bangla, Devanagari, Malayalam, Oriya, Roman and Urdu is proposed in this paper. A Block-level approach is followed for the same and initially 34-dimensional feature vector is constructed applying transform based (BRT, BDCT, BFFT and BDT), textural and statistical techniques. Finally using a GAS (Greedy Attribute Selection) method 20 attributes are selected for learning process. Total 600 unconstrained document image blocks of size 512×512 each, are prepared with equal distribution of each script type. The whole dataset is divided into 2:1 ratio for training and testing. Extensive experimentation is carried out for Six-scripts, Tetra-scripts, Tri-scripts and Bi-scripts combinations. Experimental result shows promising and comparable performance.
Applied Computational Intelligence and Soft Computing, Volume 2014, pp 1-12; https://doi.org/10.1155/2014/896128

Abstract:
Identification of script from document images is an active area of research under document image processing for a multilingual/ multiscript country like India. In this paper the real life problem of printed script identification from official Indian document images is considered and performances of different well-known classifiers are evaluated. Two important evaluating parameters, namely, AAR (average accuracy rate) and MBT (model building time), are computed for this performance analysis. Experiment was carried out on 459 printed document images with 5-fold cross-validation. Simple Logistic model shows highest AAR of 98.9% among all. BayesNet and Random Forest model have average accuracy rate of 96.7% and 98.2% correspondingly with lowest MBT of 0.09 s.
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