Abstract
The deep convolutional neural network has achieved high accuracy in handwritten Chinese character recognition (HCCR). Large scale handwritten data collection as well as labor-intensive labeling work are required to train an effective model suitable for various writing styles. Typically synthetic data are generated with data augmentation to alleviate the scarcity of labeled training data in real applications. However, the domain shift between synthetic data and real data results in unsatisfying recognition accuracy, bringing a significant challenge. A transfer learning method is proposed for synthetic-to-real handwriting recognition to alleviate the issue. In the framework, a proposed convolutional neural network is pre-trained with the synthetic data as the source model. Then, the source model is optimized with real samples of a specific handwriting style in an unsupervised manner. The effectiveness of the proposed transfer learning method is validated through systematic experiments on the public dataset as the target domain data.
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References
Ahmad, I., Fink, G.A.: Training an Arabic handwriting recognizer without a handwritten training data set. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 476–480. IEEE (2015)
Chang, B., Zhang, Q., Pan, S., Meng, L.: Generating handwritten Chinese characters using cyclegan. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 199–207. IEEE (2018)
Chen, L., Wang, S., Fan, W., Sun, J., Naoi, S.: Beyond human recognition: a CNN-based framework for handwritten character recognition. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 695–699. IEEE (2015)
Cheng, C., Zhang, X.Y., Shao, X.H., Zhou, X.D.: Handwritten Chinese character recognition by joint classification and similarity ranking. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 507–511. IEEE (2016)
Cireşan, D., Meier, U.: Multi-column deep neural networks for offline handwritten Chinese character classification. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2015)
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030–2096 (2016)
Kang, L., Rusiñol, M., Fornés, A., Riba, P., Villegas, M.: Unsupervised adaptation for synthetic-to-real handwritten word recognition. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 3491–3500. IEEE (2020)
Memon, J., Sami, M., Khan, R.A., Uddin, M.: Handwritten optical character recognition (OCR): a comprehensive systematic literature review (SLR). IEEE Access 8, 142642–142668 (2020)
Sankaranarayanan, S., Balaji, Y., Castillo, C.D., Chellappa, R.: Generate to adapt: aligning domains using generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8503–8512 (2018)
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 270–279. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7_27
Yang, H.M., Zhang, X.Y., Yin, F., Sun, J., Liu, C.L.: Deep transfer mapping for unsupervised writer adaptation. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 151–156. IEEE (2018)
Zhang, X.Y., Bengio, Y., Liu, C.L.: Online and offline handwritten Chinese character recognition: a comprehensive study and new benchmark. Pattern Recognit. 61, 348–360 (2017)
Zhang, X.Y., Liu, C.L.: Writer adaptation with style transfer mapping. IEEE Trans. Pattern Anal. Mach. Intell. 35(7), 1773–1787 (2012)
Zhang, Y., Nie, S., Liu, W., Xu, X., Zhang, D., Shen, H.T.: Sequence-to-sequence domain adaptation network for robust text image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2740–2749 (2019)
Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using googlenet and directional feature maps. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 846–850. IEEE (2015)
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Jiang, M., Zhang, B., Sun, Y., Qiang, F., Cai, H. (2021). A Transfer Learning Method for Handwritten Chinese Character Recognition. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_20
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DOI: https://doi.org/10.1007/978-981-16-2540-4_20
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