Abstract
Inspired by the recent successes of deep learning on Computer Vision and Natural Language Processing, we present a deep learning approach for recognizing scanned receipts. The recognition system has two main modules: text detection based on Connectionist Text Proposal Network and text recognition based on Attention-based Encoder-Decoder. We also proposed pre-processing to extract receipt area and OCR verification to ignore handwriting. The experiments on the dataset of the Robust Reading Challenge on Scanned Receipts OCR and Information Extraction 2019 demonstrate that the accuracies were improved by integrating the pre-processing and the OCR verification. Our recognition system achieved 71.9% of the F1 score for detection and recognition task.
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References
Zhou, X., et al.: EAST: an efficient and accurate scene text detector. In: CVPR 2017, pp. 5551–5560 (2017)
Tian, Z., Huang, W., He, T., He, P., Qiao, Y.: Detecting text in natural ımage with connectionist text proposal network. In: ECCV (2016)
Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for ımage-based sequence recognition and ıts application to scene text recognition. PAMI 2298–2304 (2016)
Le, A.D., Nakagawa, M.: Training an end-to-end system for handwritten mathematical expression recognition by generated patterns. In: ICDAR 2017, pp. 1056–1061 (2017)
Zhang, J., Du, J., Dai, L.: Multi-scale attention with dense encoder for handwritten mathematical expression recognition. In: ICPR 2018 (2018)
Le, A.D., Nguyen, H.T., Nakagawa, M.: Recognizing unconstrained vietnamese handwriting by attention based encoder decoder model. In: 2018 International Conference on Advanced Computing and Applications (ACOMP), pp. 83–87 (2018)
ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction. https://rrc.cvc.uab.es/?ch=13
CTPN Text Detection. https://github.com/eragonruan/text-detection-ctpn
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Le, A.D., Pham, D.V., Nguyen, T.A. (2019). Deep Learning Approach for Receipt Recognition. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_50
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DOI: https://doi.org/10.1007/978-3-030-35653-8_50
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