Abstract
Segmentation-free text recognition achieves successful performance because it can accurately recognize overlapping and touching characters, which is difficult for over-segmentation approaches. In contrast, character segmentation is helpful for explanations, posterior document layout analysis, and other applications. Some methods have been proposed to balance the capability of character segmentation and accurate recognition, but they cannot predict segmentation candidates for characters that can be differently segmented, which bottlenecks segmentation accuracy. In this paper, we propose Text-conditioned Character Segmentation (TCSeg) to improve segmentation accuracy. TCSeg segments characters differently according to each text candidate prediction by segmentation-free text recognition without affecting recognition accuracy. We also propose Overlap and Skip Error Suppression (OSESup) to suppress unintuitive errors using the estimated segmentation. An experiment on text recognition of handwritten Chinese characters shows that TCSeg segments characters more accurately than an existing segmentation method and that OSESup improves the recognition accuracy.
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References
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 (2006)
Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2016)
Wang, J., Hu, X.: Gated recurrent convolution neural network for OCR. In: Advances in Neural Information Processing Systems, pp. 335–344 (2017)
Liu, W., Chen, C., Wong, K.Y.K., Su, Z., Han, J.: Star-net: a spatial attention residue network for scene text recognition. In: BMVC, vol. 2, p. 7 (2016)
Strauß, T., Leifert, G., Labahn, R., Hodel, T., Mühlberger, G.: ICFHR 2018 competition on automated text recognition on a read dataset. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 477–482. IEEE (2018)
Hu, W., Cai, X., Hou, J., Yi, S., Lin, Z.: GTC: guided training of CTC towards efficient and accurate scene text recognition. In: AAAI, pp. 11005–11012 (2020)
Ingle, R.R., Fujii, Y., Deselaers, T., Baccash, J., Popat, A.C.: A scalable handwritten text recognition system. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 17–24. IEEE (2019)
Xiao, S., Peng, L., Yan, R., Wang, S.: Deep network with pixel-level rectification and robust training for handwriting recognition. SN Comput. Sci. 1(3), 1–13 (2020)
Tanaka, R., Ono, S., Furuhata, A.: Fast distributional smoothing for regularization in CTC applied to text recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 302–308. IEEE (2019)
Cheng, Z., Bai, F., Xu, Y., Zheng, G., Pu, S., Zhou, S.: Focusing attention: towards accurate text recognition in natural images. In: Proceedings of the IEEE international conference on computer vision. pp. 5076–5084 (2017)
Shi, B., Yang, M., Wang, X., Lyu, P., Yao, C., Bai, X.: Aster: an attentional scene text recognizer with flexible rectification. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2035–2048 (2018)
Baek, J., et al.: What is wrong with scene text recognition model comparisons? Dataset and model analysis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4715–4723 (2019)
Wu, Y.C., Yin, F., Liu, C.L.: Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models. Pattern Recognit. 65, 251–264 (2017)
Wang, Z.X., Wang, Q.F., Yin, F., Liu, C.L.: Weakly supervised learning for over-segmentation based handwritten Chinese text recognition. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 157–162. IEEE (2020)
Peng, D., Jin, L., Wu, Y., Wang, Z., Cai, M.: A fast and accurate fully convolutional network for end-to-end handwritten Chinese text segmentation and recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 25–30. IEEE (2019)
Qi, X., Chen, Y., Xiao, R., Li, C.G., Zou, Q., Cui, S.: A novel joint character categorization and localization approach for character-level scene text recognition. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), vol. 5, pp. 83–90. IEEE (2019)
Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2008)
Scheidl, H., Fiel, S., Sablatnig, R.: Word beam search: a connectionist temporal classification decoding algorithm. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 253–258. IEEE (2018)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)
Cong, F., Hu, W., Huo, Q., Guo, L.: A comparative study of attention-based encoder-decoder approaches to natural scene text recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 916–921. IEEE (2019)
Liu, C.L., Yin, F., Wang, D.H., Wang, Q.F.: CASIA online and offline Chinese handwriting databases. In: 2011 International Conference on Document Analysis and Recognition, pp. 37–41. IEEE (2011)
Yin, F., Wang, Q.F., Zhang, X.Y., Liu, C.L.: ICDAR 2013 Chinese handwriting recognition competition. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1464–1470. IEEE (2013)
Wang, Z.R., Du, J., Wang, J.M.: Writer-aware CNN for parsimonious hmm-based offline handwritten Chinese text recognition. Pattern Recognit. 100, 107102 (2020)
He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 558–567 (2019)
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Tanaka, R., Osada, K., Furuhata, A. (2021). Text-Conditioned Character Segmentation for CTC-Based Text Recognition. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12823. Springer, Cham. https://doi.org/10.1007/978-3-030-86334-0_10
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