Abstract
This paper introduces a project for automatically recognizing ancient Asian characters by image processing and deep learning with the aim of preserving Asian culture. The ancient characters examined include Chinese and Indian characters, which are the most mysterious, wildly used, and historic in the ancient world, and also feature multiply types. The automatic recognition method consists of preprocessing and recognition processing. The preprocessing includes character segmentation and noise reduction, and the recognition processing has a conventional recognition and deep learning. The conventional recognition method consists of feature extraction and similarity calculation or classification, and data augmentation is a key part of the deep learning. Experimental results show that deep learning achieves a better recognition accuracy than conventional image processing. Our aim is to preserve ancient literature by digitizing it and clarifying the characters and how they change throughout history by means of accurate character recognition. We also hope to help people discover new knowledge from ancient literature.
Supported by Japan Society for the Promotion of Science(JSPS) (26870713).
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References
Pu, M.Z., Xie, H.Y.: Shanghai Bo Wu Guan Cang Jia Gu Wen Zi. Shanghai Bo Wu Guan (2009)
Rubbing characters Database (Kyoto University). http://coe21.zinbun.kyoto-u.ac.jp/djvuchar. Accessed 8 Aug 2018
Meng, L., Fujikawa, Y., Ochiai, A., Izumi, T., Yamazaki, K.: Recognition of oracular bone inscriptions using template matching. Int. J. Comput. Theory Eng. 8(1), 53–57 (2016). (in Japanese)
Meng, L., Izumi, T., Oyanagi, S.: Recognition of oracular bone inscriptions by clustering and matching on the hough space. J. Inst. Image Electron. Eng. Jpn. 44(4), 627–636 (2015). (in Japanese)
Meng, L.: Recognition of oracle bone inscriptions by extracting line features on image processing. In: Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017), pp. 606–611 (2017)
Meng, L.: Two-stage recognition for oracle bone inscriptions. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10485, pp. 672–682. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68548-9_61
Meng, L.: Recognition of Oracle Bone Inscriptions Using Image Processing, 1st edn. HOKUTO PRINT, Kyoto (2016). (in Japanese)
Ochiai, A.: Oracle Bone Inscriptions Database. http://koukotsu.sakura.ne.jp/top.html. Accessed 8 Aug 2018
Kamitoku, N., Meng, L., Yamazaki, K.: Recognition of oracle bone inscriptions using deep learning. In: The 80th National Convention of Information Processing Society of Japan, 2S-07 (2018). (in Japanese)
Watanabe, S., Meng, L., Izumi, T.: Methods to extract character regions of oracle bone inscriptions. In: The 248th Technical Report of the Institute of Image Electronics Engineers of Japan (2018). (in Japanese)
Kishi, M., Nabeya, M., Nogami, K., Meng, L., Yamazaki, K.: Multi-typeface recognition of rubbing using deep learning and creation of spatiotemporal database. In: The 80th National Convention of Information Processing Society of Japan, 2S-08 (2018). (in Japanese)
Aravinda, C.V., Prakash, H.N.: A review on various HandWritten cursive characters segmentation techniques. In: 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 647–651 (2016)
Aravinda, C.V., Prakash, H.N.: A review recognition for south india languages using statistical feature extraction and distance classifier. Int. J. Nat. Lang. Comput. (IJNLC) 5(4), 37–48 (2016)
Aravinda, C.V., Prakash, H.N.: Template matching method for Kannada Handwriten recognition based on correlation analysis. In: 2014 International Conference on Contemporary Computing and Informatics (IC3I), pp. 857–861 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25 (NIPS 2012) (2012)
Szegedy, C., et al.: Going deeper with convolutions. In : 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015) (2015)
Aravinda, C.V., Prakash, H.N.: A machine editable format for Kannada handwritten character recognition using a dissect and connect technique. IJETAE 4, 1–5 (2014)
Aravinda, C.V., Prakash, H.N., Lavanya, S.: Kannada handwritten character recognition using multi feature extraction technique. Int. J. Sci. Res. (IJSR) 3(10), 911–916 (2014)
Aravinda, C.V., Prakash, H.N.: Template matching method for kannada handwritten recognition based on correlation analysis. In: 2014 International Conference on Contemporary Computing and Informatics (IC3I) (2014)
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Meng, L., Aravinda, C.V., Uday Kumar Reddy, K.R., Izumi, T., Yamazaki, K. (2018). Ancient Asian Character Recognition for Literature Preservation and Understanding. In: Ioannides, M., et al. Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection. EuroMed 2018. Lecture Notes in Computer Science(), vol 11196. Springer, Cham. https://doi.org/10.1007/978-3-030-01762-0_66
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