Abstract
Handwritten recognition is a difficult task. The conventional technique relies on the character segmentation, feature extraction, and classification process. The segmentation is a tremendous challenge when there are variation of character patterns and alignments in a sentence, such as linking segments between characters in the Thai language. Promising segmentation outcome is favorable but not applicable in most applications. This work proposes a methodology for Thai handwritten recognition by applying Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The first step is text localization before feeding to the network. CNN extracts the abstract features before they are fed to RNN to learn the sequence of characters in an image. The optimization is performed with an integrated Connectionist Temporal Classification (CTC) module (to arrange the final results). A standard Thai handwritten dataset (BEST2019) and more collection are used in this study. for training and test sets. The experimental results show that the integration of CNN and RNN provides promising results of the test set with a Character Error Rate (CER) of 1.58%. For testing with the seen and unseen dataset of the final round of BEST2019 competition, the CER is at 24.53%.
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Change history
31 July 2021
The word ‘encoded’ has been updated to ‘decoded’ in section 4.3 of the chapter.
References
NECTEC.: OCR ArnThai service, http://arnthai.nectec.or.th/. Accessed 28 Feb 2015
LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 253–256 (2010)
Bouchain, D.: Character recognition using convolutional neural networks. In: Institute for Neural Information Processing (2007)
Simard, P. Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Proceedings of the 7th International Conference on Document Analysis and Recognition, pp. 958–963. (2003)
Lauer, F., Suen, C.Y., Bloch, G.: A trainable feature extractor for handwritten digit recognition. Pattern Recogn. 40(6), 1816–1824 (2007)
Breuel, T., Frinken V., Liwicki, M.: High-performance OCR for printed English and Fraktur using LSTM networks. In: Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 683–687.(2013)
Smith, R.: Tesseract-ocr/docs, https://github.com/tesseract-ocr/docs/tree/master/das_tutorial2016. Accessed 22 Dec 2017
Sinthupinyo, W.: Benchmark for Enhancing the Standard Thai Language Processing (BEST). https://thailang.nectec.or.th/best/best2019-handwrittenrecognition-objective/. Accessed 15 May 2019
Chamchong, R., Fung, C.C.: Text line extraction using adaptive partial projection for palm leaf manuscripts from Thailand. In: Proceeding of 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 588–593 (2012)
Karnchanapusakij, C., Suwannakat, P., Rakprasertsuk W., Dejdumrong, N.: Online handwriting thai character recognition. In: The 6th International Conference on Computer Graphics, Imaging and Visualization, Tianjin, pp. 323–328 (2009)
Methasate, I., Sae-tang, S.: The clustering technique for thai handwritten recognition. In: Proceeding of International Workshop on Frontiers in Handwriting Recognition, Japan, pp. 450–454. (2004)
NECTEC, NSTDA, https://aiforthai.in.th/index.php. Accessed 20 Dec 2019
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations, pp. 730–734 (2015)
Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd International Conference on Machine learning, pp. 369–376. ACM (2006)
Deng, H., Stathopoulos, G., Suen, C.Y.: Error correcting output coding for the convolutional neural network for optical character recognition. In: Proceeding of the 10th International Conference on Document Analysis and Recognition, pp. 581–585 (2009)
Bluche, T.: Deep Neural Networks for Large Vocabulary Handwritten Text Recognition (2015)
Chollet, F. Keras: the Python deep learning API, https://keras.io/. Accessed 23 Feb 2020
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Chamchong, R., Saisangchan, U., Pawara, P. (2021). Thai Handwritten Recognition on BEST2019 Datasets Using Deep Learning. In: Chomphuwiset, P., Kim, J., Pawara, P. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2021. Lecture Notes in Computer Science(), vol 12832. Springer, Cham. https://doi.org/10.1007/978-3-030-80253-0_14
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