ABSTRACT
For handwritten character recognition, a common problem is that each writer has unique handwriting for each character (e.g. stroke, head, loop, and curl). The similarities of handwritten characters in each language is also a problem. These similarities have led to recognition mistakes. This research compared deep Convolutional Neural Networks (CNNs) which were used for handwriting recognition in the Thai language. CNNs were tested with the THI-C68 dataset. This research also compared two training methods, Train from scratch and Transfer learning, by using VGGNet-19 and Inception-ResNet-v2 architectures. The results showed that VGGNet-19 architecture with transfer learning can reduce learning time. Moreover, it also increased recognition efficiency up to 99.20% when tested with 10-fold cross-validation.
- Deng, J., Dong, W., Socher, R., Li, L.-J., Kai Li and Li Fei-Fei 2009. ImageNet: A large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Jun. 2009), 248--255.Google ScholarCross Ref
- He, K., Zhang, X., Ren, S. and Sun, J. 2016. Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Dec. 2016), 770--778.Google Scholar
- Hu, J., Shen, L. and Sun, G. 2018. Squeeze-and-Excitation Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Dec. 2018), 7132--7141.Google Scholar
- Huang, G., Liu, Z., Maaten, L. van der and Weinberger, K.Q. 2017. Densely Connected Convolutional Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Nov. 2017), 2261--2269.Google Scholar
- Inkeaw, P., Bootkrajang, J., Marukatat, S., Gonçalves, T. and Chaijaruwanich, J. 2019. Recognition of Similar Characters using Gradient Features of Discriminative Regions. Expert Systems with Applications. 134, (Nov. 2019), 120--137.Google ScholarDigital Library
- Ioffe, S. and Szegedy, C. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. The 32nd International Conference on International Conference on Machine Learning (2015), 448--456.Google Scholar
- Joseph, F.J.J. and Anantaprayoon, P. 2018. Offline Handwritten Thai Character Recognition Using Single Tier Classifier and Local Features. The 3rd International Conference on Information Technology (InCIT) (Oct. 2018), 8--11.Google Scholar
- Kim, I.J. and Xie, X. 2014. Handwritten Hangul Recognition using Deep Convolutional Neural Networks. International Journal on Document Analysis and Recognition. 18, 1 (2014), 1--13.Google ScholarDigital Library
- Krizhevsky, A., Sutskever, I. and Hinton, G.E. 2012. ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25 (2012), 1090--1098.Google Scholar
- Lecun, Y., Bengio, Y. and Hinton, G. 2015. Deep learning. Nature. 521, 7553 (May 2015), 436--444.Google Scholar
- Lecun, Y., Bottou, L., Bengio, Y. and Haffner, P. 1998. Gradient-Based Learning Applied to Document Recognition. IEEE. 86, 11 (1998), 2278--2324.Google ScholarCross Ref
- Marinai, S. 2008. Introduction to Document Analysis and Recognition. Studies in Computational Intelligence. Springer Verlag. 1--20.Google Scholar
- Nair, V. and Hinton, G.E. 2010. Rectified Linear Units Improve Restricted Boltzmann Machines Vinod Nair. The 27th International Conference on Machine Learning (2010), 807--814.Google Scholar
- Okafor, E., Pawara, P., Karaaba, F., Surinta, O., Codreanu, V., Schomaker, L. and Wiering, M. 2016. Comparative Study between Deep Learning and Bag of Visual Words for Wild-Animal Recognition. IEEE Symposium Series on Computational Intelligence (SSCI) (Dec. 2016), 1--8.Google Scholar
- Ruder, S. 2016. An Overview of Gradient Descent Optimization Algorithms. (Sep. 2016), 1--14.Google Scholar
- Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. and Chen, L.C. 2018. MobileNetV2: Inverted Residuals and Linear Bottlenecks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Dec. 2018), 4510--4520.Google Scholar
- Sawada, Y. and Kozuka, K. 2016. Whole Layers Transfer Learning of Deep Neural Networks for a Small Scale Dataset. International Journal of Machine Learning and Computing. 6, 1 (2016), 27--31.Google Scholar
- Simonyan, K. and Zisserman, A. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations (ICLR).Google Scholar
- Surinta, O., Karaaba, M.F., Schomaker, L.R.B. and Wiering, M.A. 2015. Recognition of handwritten characters using local gradient feature descriptors. Engineering Applications of Artificial Intelligence. 45, (Oct. 2015), 405--414.Google ScholarDigital Library
- Szegedy, C., Ioffe, S., Vanhoucke, V. and Alemi, A. 2017. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. The Thirty-First AAAI Conference on Artificial Intelligence (2017), 4278--4284.Google Scholar
- Wang, B. and Klabjan, D. 2017. Regularization for Unsupervised Deep Neural Nets. The Thirty-First AAAI Conference on Artificial Intelligence (Aug. 2017), 2681--2687.Google ScholarCross Ref
Index Terms
- Improving Recognition of Thai Handwritten Characters with Deep Convolutional Neural Networks
Recommendations
RATNet: A deep learning model for Bengali handwritten characters recognition
AbstractThe Bengali language is based on a set of symbols for basic characters, modifiers, compound characters, and numerals. The recognition rates of handwritten basic characters and numerals are very high. However, the recognition rates of compound ...
Handwritten Cursive Jawi Character Recognition: A Survey
CGIV '08: Proceedings of the 2008 Fifth International Conference on Computer Graphics, Imaging and VisualisationThe subject of cursive handwritten character recognition is still open to be studied because of its complex nature. Recognition of Arabic handwritten and its variants such as Farsi (Persian) and Urdu had been receiving considerable attention in recent ...
Unconstrained handwritten Devanagari character recognition using convolutional neural networks
MOCR '13: Proceedings of the 4th International Workshop on Multilingual OCRIn this paper, we introduce a novel offline strategy for recognition of online handwritten Devanagari characters entered in an unconstrained manner. Unlike the previous approaches based on standard classifiers - SVM, HMM, ANN and trained on statistical, ...
Comments