Abstract
Extracting knowledge from ancient palm leaf manuscripts is essential for historians and other scholars who would like to access accumulated knowledge in the Thai Noi language manuscripts. In the absence of Thai Noi language readers, computer technologies play an important role in fulfilling this need. This research aims to apply deep learning approaches to recognize Thai Noi characters written in palm leaf manuscripts. The experiments were carried out by firstly collecting the page images of the manuscripts archived in the Museum of Art and Culture of Loei. Then the page images were preprocessed by converting to grayscale. To recognize Thai Noi characters, four convolutional neural network models based on inception and mobilenet networks namely Inception-v3, Inception-v4, MobileNetV1, and MobileNetV2 were evaluated. Handwritten Thai Noi characters were segmented from the grayscale images based on 26 Thai Noi characters. In this process, 100 images of each character were segmented and the whole dataset contained 2,600 images. Two image augmentation methods were applied to increase the amount of training data. Three experiments were carried out with three different datasets based on a 10-fold cross-validation design. The results indicate that MobileNetV1 outperformed other models in all experiments with an accuracy rate higher than 90%, while MobileNetV2 showed an interesting performance, which was almost equivalent to MobileNetV1 in the last experiment.
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References
Szegedy, C., et al.: Going deeper with convolutions. aXiv preprntari:109442 7 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence, pp. 4278–4284 (2017)
Howard, et al.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv preprint arXiv:1704.04861 (2017)
Vijaya Lakshmi, T.R., Sastry, P.N., Rajinikanth, T.V.: Feature selection to recognize text from palm leaf manuscripts. Signal, Image Video Process. 12(2), 223–229 (2017). https://doi.org/10.1007/s11760-017-1149-9
Inkeaw, P., Chueaphun, C., Chaijaruwanich, J., Klomsae, A., Marukatat, S.: Lanna Dharma handwritten character recognition on palm leaves manuscript based on wavelet transform. In: 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 253–258. IEEE (2015)
Valy, D., Verleysen, M., Chhun, S., Burie, J.C.: Character and text recognition of Khmer historical palm leaf manuscripts. In: 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 13–18. IEEE (2018)
Kesiman, M.W.A., Prum, S., Burie, J.C., Ogier, J.M.: Study on feature extraction methods for character recognition of Balinese script on palm leaf manuscript images. In: 23rd International Conference on Pattern Recognition (ICPR), pp. 4017–4022. IEEE (2016)
Puarungroj, W., Boonsirisumpun, N.: Recognizing hand-woven fabric pattern designs based on deep learning. In: Bhatia, S.K., Tiwari, S., Mishra, K.K., Trivedi, M.C. (eds.) Advances in Computer Communication and Computational Sciences. AISC, vol. 924, pp. 325–336. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-6861-5_28
Chueaphun, C., Klomsae, A., Marukatat, S., Chaijaruwanich, J.: Lanna Dharma printed character recognition using k-nearest neighbor and conditional random fields. In: Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR) SCITEPRESS, pp. 169–174 (2012)
Puarungroj, W., Kulna, P., Soontarawirat, T., Boonsirisumpun, N.: Recognition of Thai Noi characters in palm leaf manuscripts using convolutional neural network. In: Asia-Pacific Conference on Library & Information Education and Practice (A-LIEP), pp. 408–415 (2019)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobilenetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
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Puarungroj, W., Boonsirisumpun, N., Kulna, P., Soontarawirat, T., Puarungroj, N. (2020). Using Deep Learning to Recognize Handwritten Thai Noi Characters in Ancient Palm Leaf Manuscripts. In: Ishita, E., Pang, N.L.S., Zhou, L. (eds) Digital Libraries at Times of Massive Societal Transition. ICADL 2020. Lecture Notes in Computer Science(), vol 12504. Springer, Cham. https://doi.org/10.1007/978-3-030-64452-9_20
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DOI: https://doi.org/10.1007/978-3-030-64452-9_20
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