Use of Metric Learning for the Recognition of Handwritten Digits, and its Application to Increase the Outreach of Voice-based Communication Platforms

Pant, Devesh; Talukder, Dibyendu; Kumar, Deepak; Pandey, Rachit; Seth, Aaditeshwar; Arora, Chetan

doi:10.1145/3530190.3534795

ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) 2022

DOI: 10.1145/3530190.3534795

|View full text |Cite

Use of Metric Learning for the Recognition of Handwritten Digits, and its Application to Increase the Outreach of Voice-based Communication Platforms

Devesh Pant

Dibyendu Talukder²,

Deepak Kumar³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Robust OCR Pipeline for Automated Digitization of Mother and Child Protection Cards in India

Pant,

Talukder,

Seth

et al. 2023

ACM J. Comput. Sustain. Soc.

View full text Add to dashboard Cite

The Universal Immunization Programme (UIP) in India has a mandate to fully vaccinate all of India’s 27 million children born annually. The vaccination doses are recorded by frontline health workers on standardized paper-based Mother and Child Protection (MCP) cards, which are manually digitized by data entry operators, resulting in poor data quality, delays, and significant time and resources. In our paper, we focus on Optical Character Recognition (OCR) based automated digitization of MCP card images captured through a smartphone application developed by us. By utilizing a standardized template for the MCP cards, which is available a-priori, we register the card images and perform OCR on the extracted region of interest (ROIs). Since the cards with curvature or torn edges had poor ROIs, we built a global-local alignment technique which first approximates the ROI using global Homography and then refines using a local Homography resulting in improved accuracy. Our pipeline gives a character level accuracy of 98.73% on our dataset, against 75.02% by Google Cloud Vision and 79.26% by Azure OCR. We also describe our field testing experience, where the digitized MCP card images were used to provide useful features on the smartphone application for health workers to conduct vaccination sessions.

show abstract

Robust OCR Pipeline for Automated Digitization of Mother and Child Protection Cards in India

Pant,

Talukder,

Seth

et al. 2023

ACM J. Comput. Sustain. Soc.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Use of Metric Learning for the Recognition of Handwritten Digits, and its Application to Increase the Outreach of Voice-based Communication Platforms

Cited by 1 publication

References 20 publications

Robust OCR Pipeline for Automated Digitization of Mother and Child Protection Cards in India

Robust OCR Pipeline for Automated Digitization of Mother and Child Protection Cards in India

Contact Info

Product

Resources

About