Abstract
In last decades, methods based on deep learning has dominated many application fields, such as image inpainting, image super high-resolution and image denoising. In the Optical Character Recognition (OCR), we need to erase all seals due to some special reason like improving the performance of invoices and receipts recognition. However, owing to the diversity of the shape, color and position of the seals, it is very difficult to erase all seals without affecting the original information of the invoices and receipts. At present, there are two common methods to erase seals, one is from the perspective of computer graphics, the other is from the perspective of computer software, like PhotoShop. Nevertheless, the common weakness is the poor robustness and inefficient. So, In this paper, We propose an end-to-end network for erasing seals, and we have four contributions. (1) We first propose a universal framework to do the seals erasing by using Generative Adversarial Network (GAN). (2) Training the seals erasing network by the images synthesised by scripts and seals crawled on the website, instead of spending high cost to collect images by ourselves. (3) In terms of speed, due to using end-to-end predict script, using our method to erase all seals on the 512 * 512 RGB image with padding is only 800 ms, much faster than the traditional methods. (4) There is also improvement on the invoices and receipts with the same parameter on the Convolution Recurrent Neural Network (CRNN), the average accuracy of the sequences by using our method is 91.3% higher than the original images is 89.7% on the 800 quota invoice test images.
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
Similar content being viewed by others
References
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 (2015)
Tian, Z., Huang, W., He, T., He, P., Qiao, Y.: Detecting text in natural image with connectionist text proposal network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 56–72. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_4
Wojna, Z., Gorban, A.N., Lee, D.S., et al.: Attention-based extraction of structured information from street view imagery. In: IEEE 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) - Kyoto, 9–15 November 2017, pp. 844–850 (2017)
Goodfellow, I.: NIPS 2016 tutorial: generative adversarial networks (2016)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. Comput. Sci. 2672–2680 (2014)
Isola, P., Zhu, J.Y., Zhou, T., et al.: Image-to-image translation with conditional adversarial networks (2016)
Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks (2017)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection (2015)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: International Conference on Neural Information Processing Systems (2015)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization (2016)
Li, C., Wand, M.: Precomputed real-time texture synthesis with markovian generative adversarial networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 702–716. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_43
Graves, A.: Connectionist temporal classification. In: Graves, A. (ed.) Supervised Sequence Labelling with Recurrent Neural Networks. SCI, vol. 385, pp. 61–93. Springer, Heidelberg (2012)
Wang, X., Man, Z., You, M., et al.: Adversarial generation of training examples: applications to moving vehicle license plate recognition (2017)
Rez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM Trans. Graph. 22(3), 313–318 (2003)
Hinton, G., Srivastava, N., Swersky, K.: Lecture 6D - a separate, adaptive learning rate for each connection. Slides Lect. Neural Netw. Mach. Learn. (2012)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)
Acknowledgements
The work is partially supported by School of Software, University of Science and Technology of China. We also acknowledge to the authors of the real image examples but we do not own the copyrights of them to verify the performance of our method.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, Z., Ding, Z., Wang, S. (2019). Universal Framework of Seals Erasing with Generative Adversarial Network. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_27
Download citation
DOI: https://doi.org/10.1007/978-981-13-9917-6_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9916-9
Online ISBN: 978-981-13-9917-6
eBook Packages: Computer ScienceComputer Science (R0)