The Microservice-based Optical Character Recognition application for offline forms

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Jayeesha Ghosh
Srivathshan KS
Parvej Saleh

Abstract

The majority of Indian offices still use the old school method of filling up paper-based forms and storing them. They need human interference even when digitalized. The use of
digitalized data in the future is inevitable as it makes easier to be interpreted and stored. In this paper, we focus on the extension and user-based implementation of the previously inhouse developed OCR model mentioned in the published paper 'FormAssist' [1]. Making the model user-friendly through a web application called OCR-WebApp using microservices
architecture and converting it into an end-to-end (E2E) business solution. We explain how the input image of offline filled form data flow from the user end to the controller through API
calls, passes through the model, processed in serverless architecture, results are stored in cloud storage and displayed to the user, all done in a few minutes. We also discuss the challenges encountered while deploying the model into a production level architecture and how we overcame those.

Article Details

How to Cite
GhoshJ., KSS., & SalehP. (2020). The Microservice-based Optical Character Recognition application for offline forms. Probyto Journal of AI Research, 1(01). Retrieved from https://journal.probyto.com/index.php/probyto-ai-research/article/view/12
Section
Articles

References

[1] Srivathsan KS et al., "FormAssist: Deep learning method for converting handwritten forms into digital
assets" in DCAL National Conference on Machine Learning and Artificial Intelligence August 27-28, 2018.
[2] Lianping Chen. "Microservices: Architecting for Continuous Delivery and DevOps". in IEEE International
Conference on Software Architecture. 2018. Seattle, USA: IEEE
[3] A. Jindal and M. Amir, “Automatic classification of handwritten and printed text in ICR boxes,” Souvenir
2014 IEEE Int. Adv. Comput. Conf. IACC 2014, pp. 1028– 1032, 2014.
[4] Bhatia, "Optical Character Recognition Techniques: A Review", International Journal of Advanced Research
in Computer Science and Software Engineering 4(5), May - 2014, pp. 1219-1223
[5] Batuhan Balci, Dan Saadati, Dan Shiferaw, "Handwritten Text Recognition using Deep Learning"
[6] Samuel Stallin Kapembe and José Quenum, “Lihonga — a Microservice-based Virtual Learning
Environment” in 18th International Conference on Advanced Learning Technologies, 2018, IEEE.
[7] Deeraj Nagothu et al., “A Microservice-enabled Architecture for Smart Surveillance using Blockchain
Technology”, IEEE.

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