Abstract
Optical Character Recognition (OCR) plays an essential role in nowadays life, which contributes to solving problems in terms of timing and accuracy of documents. The use of OCR in the health sector can help solve problems of drug handling or inventorying in drug banks to prevent unnecessary risks. However, if you apply existing OCR meth- ods such as Tesseract or EasyOCR, it will be challenging to find out the name of the medicine or the medicine ingredient in a prescription. In this paper, we propose a system to help find the medicine names from the prescription image. We then provide users with information on the identified medicine names. Methods are built by combining and transforming many existing identity models. In addition, we have successfully developed an application running on the Android platform to get feedback on improving the system and want to help them get more information about the drugs they are using. Experimental results show that the model recognizes drug names quite well on a given database, even with medium resolution photos.
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
Notes
- 1.
Flutter is a free and open-source mobile UI framework created by Google and released in May 2017. In a few words, it allows you to create a native mobile application with only one codebase. This means that you can use one programming language and one codebase to create two different apps (for iOS and Android).
- 2.
References
Patel, C., Patel, A., Patel, D.: Optical character recognition by open source OCR tool tesseract: a case study. Int. J. Comput. Appl. 55(10), 50–56 (2012)
Balažević, I., Allen, C., Hospedales, T.M.: Hypernetwork knowledge graph embeddings. In: Tetko, I.V., Kůrková, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11731, pp. 553–565. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30493-5_52
Smith, R.: An overview of the tesseract OCR engine. In: Ninth international conference on document analysis and recognition (ICDAR 2007), vol. 2, pp. 629–633. IEEE (2007)
Zacharias, E., Teuchler, M. and Bernier, B.: Image Processing Based Scene-Text Detection and Recognition with Tesseract. arXiv preprint arXiv:2004.08079, (2020)
Baek, Y., Lee, B., Han, D., Yun, S., Lee, H.: Character region awareness for text detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9365–9374 (2019)
Huang, M., Lan, C., Huang, W., Tao, Y.: Natural scene text detection based on multiscale connectionist text proposal network. J. Eng. 2020(13), 326–329 (2020)
Huang, C., Xu, J.: An anchor-free oriented text detector with connectionist text proposal network. In: Asian Conference on Machine Learning, pp. 631–645. PMLR (October 2019)
Shen, Z., Zhang, R., Dell, M., Lee, B.C.G., Carlson, J., Li, W.: LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis. arXiv preprint arXiv:2103.15348 (2021)
Zhang, S., Hu, Y., Bian, G.: Research on string similarity algorithm based on levenshtein distance. In: 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 2247–2251 (2017)
Lhoussain, A.S., Hicham, G.U.E.D.D.A.H., Abdellah, Y.O.U.S.F.I.: Adaptating the levenshtein distance to contextual spelling correction. Int. J. Comput. Sci. Appl. 12(1), 127–133 (2015)
Hicham, G.: Introduction of the weight edition errors in the Levenshtein distance. arXiv preprint arXiv:1208.4503 (2012)
Baek, Y., Lee, B., Han, D., Yun, S., Lee, H.: Character region awareness for text detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9365–9374 (2019)
Levenshtein Distance. https://en.wikipedia.org/wiki/Levenshtein_distance. Accessed 15 Apr 2021
Vietnam Drug Bank. https://drugbank.vn/danh-sach-thuoc. Accessed 15 Apr 2021
FuzzyWuzzy. https://openlibrary-repo.ecampusontario.ca/jspui/bitstream/1456789. Accessed 15 Apr 2021
Flutter. https://flutter.dev. Accessed 15 Apr 2021
EasyOCR. https://github.com/JaidedAI/EasyOCR. Accessed 15 Apr 2021
Tesseract documentation. https://tesseract-ocr.github.io/tessdoc/ImproveQuality. Accessed 15 Apr 2021
Tesseract code. https://github.com/tesseract-ocr/tesseract. Accessed 15 Apr 2021
Tesseract OCR with Python. https://artificialintelligence.oodles.io/blogs/tesseract-ocr-with-python/. Accessed 15 Apr 2021
Acknowledgement
This research is funded by Advanced Program in Computer Science, the Faculty of Information Technology, University of Science, VNU-HCM, Vietnam.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, TT., Nguyen, DV.V., Le, T. (2021). Developing a Prescription Recognition System Based on CRAFT and Tesseract. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-88081-1_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88080-4
Online ISBN: 978-3-030-88081-1
eBook Packages: Computer ScienceComputer Science (R0)