Abstract
The handwritten Tamil character recognition in offline mode is challenging tasks as there are virtually different people who have different styles of writing the same characters. Deep convolution neural networks are playing a virtual role nowadays in recognizing handwritten character by automatically learning discriminative features from high dimensionality of input data. This work presents a modified convolution neural network \(\left( \text {M-CNN} \right) \) architecture to achieve a faster convergence rate and also to get the highest recognition accuracy. The M-CNN on different aspects along with layers design, activation function, loss function and optimization is discussed. Systematic experiments on isolated handwritten Tamil character dataset collected from various schools by ourselves. For these collected datasets, the proposed system recognized the characters with 97.07%.
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References
Thadchanamoorthy S, Kodikara ND, Premaretne HL, Pal U, Kimura F (2013) Tamil handwritten city name database development and recognition for postal automation. In: IEEE-ICDAR, pp 793–797
Feng B, Ren M, Zhang X, Suen C (2014) Automatic recognition of serial numbers in bank notes. Patt Recogn 47:2621–2634
Zhang X-Y, Bengio Y, Liu C-L (2017) Online and offline handwritten chinese character recognition: a comprehensive study and new benchmark. Patt Recogn 61:348–60
Ashay Singh and Ankur Singh Bist (2019) A wide scale survey on handwritten character recognition using machine learning. Int J Comput Sci Eng 7(6):124–134
Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T et al (2018) Recent advances in convolutional neural networks. 77:354–377
Raghupathy KB, Chandrasekaran S (2019) Benchmarking on offline Handwritten Tamil Character Recognition using convolutional neural networks. J King Saud Univ Comput Informa Sci
Sureshkumar C, Ravichandran T (2010) Handwritten Tamil character recognition and conversion using neural network. Int J Comput Sci Eng 02(07):2261–2267
Vinotheni C, Lakshmana Pandian L (2019) A state of art approaches on handwriting recognition models. In: IEEE fifth international proceedings on science technology engineering and mathematics, vol 1, pp 98–103
Yang W, Jin L, Tao D, Xie Z, Feng Z (2016) A new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten Chinese character recognition. Patt Recogn 58:190–203
Zhang Y, Liang S, Nie S, Liu W, Peng S (2018) Robust offline handwritten character recognition through exploring writer-independent features under the guidance of printed data. Patt Recogn Lett 106:20–26
Li Z, Teng N, Jin M, Lu H (2016) Building efficient CNN architec-ture for offline handwritten Chinese character recognition. Int J Doc Anal Recogn (IJDAR) 21(4):233–240
Boufenar C, Kerboua A, Batouche M (2018) Investigation on deep learning for off-line handwritten Arabic character recognition. Cogn Syst Res 50:180–195
El-Sawy A, Loey M, Hazem EB (2017) Arabic handwritten characters recognition using convolutional neural network (2017) . WSEAS Trans Comput Res 5:11–19
Roy S, Das N, Kundu M, Nasipuri M (2017) Handwritten isolated Bangla compound character recognition: a new benchmark using a novel deep learning approach. Patt Recogn Lett 90:15–21
Jangid M, Srivastava S (2018) Handwritten Devanagari character recognition using layer-wise training of deep convolutional neural networks and adaptive gradient methods. J Imag 4(2):1–14
Raymond P, Petroski F, Such SP, Brockler F, Singh V, Hutkowski P (2018) Intelligent character recognition using fully convolutional neural net-works. Patt Recogn 88:604–613
Davit S, Magda T, Levan S, Irakli K, Shota A, Sandro J (2018) On Georgian handwritten character recognition. IFAC-PapersOnLine 51(30):161–165
Deepa M, Deepa R, Meena R, Nandhini R (2019) Tamil handwritten text recognition using convolutional neural networks. Int J Eng Sci Comput 9(3):20986–20988
Vijayaraghavan, Prashanth, and Misha Sra: Handwritten Tamil recognition using a convolutional neural network. MIT Media Lab, (2014)
Niu X-X, Suen CY (2012) A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Patt Recogn 45(4):1318–1325
Sahare P, Dhok SB (2018) Multilingual character segmentation and recognition schemes for Indian document images. IEEE Access 6:10603–10617
Pragathi MA, Priyadarshini K, Saveetha S, Shavar Banu A, Mohammed Aarif KO (2019) Handwritten Tamil Character Recognition using deep learning. In: IEEE international proceedings on vision towards emerging trends in communication and networking (ViTECoN), pp 1–5
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS et al (2016) Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467
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Vinotheni, C., Lakshmana Pandian, S., Lakshmi, G. (2021). Modified Convolutional Neural Network of Tamil Character Recognition. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_46
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DOI: https://doi.org/10.1007/978-981-15-4218-3_46
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