Abstract
Ancient Cham glyphs have mostly appeared in inscriptions on stones at some museums in Vietnam. Unfortunately, these inscriptions are being abrasive by the time. To conserve Cham heritage as well as to make them widely accessible and readable by users, digitization and recognition of ancient Cham glyphs become necessary. In our previous work, we have built the first dataset of champ inscription images, manually segmented them in glyphs and annotated by an ancient Cham expert. We adapted some automatic recognition methods and conducted experiments on the manually denoised dataset. The aim of this paper is to extend that earlier research to work on noising data. To this end, we face two main issues. Firstly, the current pre-built dataset is still small which is usually a main drawback for deep learning based methods. Therefore, some data augmentation techniques will be evaluated and investigated to increase the number and variation of samples in the dataset. Second, even with the augmented dataset, the fact of training a deep model from scratch could be very long and sometimes cannot meet a good local minimum. Therefore, we use a simple transfer learning procedure which inherits knowledge from similar or of the same family language. Experiments on both the raw test set and its denoised version show very promising results (\(64.4\%\) and \(88.5\%\) of F1-score on two test sets respectively).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Valy, D., Verleysen, M., Chhun, S., Burie, J.C.: A new Khmer Palm leaf manuscript dataset for document analysis and recognition: SleukRith set. In: Proceedings of the 4th International Workshop on Historical Document Imaging and Processing, pp. 1–6. ACM (2017)
Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 (2017)
Xu, N., Wang, W., Qu, X.: On-line sample generation for in-air written Chinese character recognition based on leap motion controller. In: Ho, Y.-S., Sang, J., Ro, Y.M., Kim, J., Wu, F. (eds.) PCM 2015. LNCS, vol. 9314, pp. 171–180. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24075-6_17
Leung, K.C., Leung, C.H.: Recognition of handwritten Chinese characters by combining regularization, fisher’s discriminant and distorted sample generation. In: Proceedings of the 10th International Conference on Document Analysis and Recognition, pp. 1026–1030 (2009)
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: ICDAR, vol. 3, no. 2003 (2003)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893 (2005)
Junior, O.L., Delgado, D., Gonçalves, V., Nun, U.: Trainable classifier-fusion schemes: an application to pedestrian detection. In: Intelligent Transportation Systems, vol. 2 (2009)
Takahashi, K., Takahashi, S., Cui, Y., Hashimoto, M.: Remarks on computational facial expression recognition from HOG features using quaternion multi-layer neural network. In: Mladenov, V., Jayne, C., Iliadis, L. (eds.) EANN 2014. CCIS, vol. 459, pp. 15–24. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11071-4_2
Griffith, A., Lepoutre, A., Southworth, W.A., Thanh, P.: The Inscriptions of Campa at the Museum of Cham Sculpture in Da Nang. VNU-HCM Publishing House (2012)
Schweyer, A.-V.: Chronologie des inscriptions publiées du Campā. In: Bulletin de l’Ecole française d’Extrême-Orient, vol. 86, pp. 321–344 (1999)
Kesiman, M., et al.: Benchmarking of document image analysis tasks for palm leaf manuscripts from Southeast Asia. J. Imaging 4(2), 43 (2018)
Nguyen, M.-T., Schweyer, A.-V., Le, T.-L., Tran, T.-H., Vu, H.: Preliminary results on ancient cham character recognition from cham inscription images. In: 2nd International Conference on Multimedia Analysis and Pattern Recognition (2019)
Nordlöf, J.: Comparative Analysis of Models for Real-Time Pattern Recognition (2014)
Zhu, X., Liu, Y., Li, J., Wan, T., Qin, Z.: Emotion classification with data augmentation using generative adversarial networks. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10939, pp. 349–360. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93040-4_28
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)
Fei-Fei, L.: ImageNet: crowdsourcing, benchmarking and other cool things. In: CMU VASC Seminar, vol. 16, pp. 18–25 (2010)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350(6266), 1332–1338 (2015)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Kumar, S.: Neighborhood pixels weights-a new feature extractor. Int. J. Comput. Theory Eng. 2(1), 69 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, MT., Schweyer, AV., Le, TL., Tran, TH., Vu, H. (2019). Improving Ancient Cham Glyph Recognition from Cham Inscription Images Using Data Augmentation and Transfer Learning. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds) New Trends in Image Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science(), vol 11808. Springer, Cham. https://doi.org/10.1007/978-3-030-30754-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-30754-7_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30753-0
Online ISBN: 978-3-030-30754-7
eBook Packages: Computer ScienceComputer Science (R0)
