Machine Learning
Essential JNI: Java Native Interface
Essential JNI: Java Native Interface
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
XRDS: Crossroads, The ACM Magazine for Students - The Future of Interaction
ICTD'09 Proceedings of the 3rd international conference on Information and communication technologies and development
Local ground: a paper-based toolkit for documenting local geo-spatial knowledge
Proceedings of the First ACM Symposium on Computing for Development
Open data kit: tools to build information services for developing regions
Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development
Managing microfinance with paper, pen and digital slate
Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development
Combating rural child malnutrition through inexpensive mobile phones
Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design
The design and implementation of the PartoPen maternal health monitoring system
Proceedings of the 3rd ACM Symposium on Computing for Development
Open data kit 2.0: expanding and refining information services for developing regions
Proceedings of the 14th Workshop on Mobile Computing Systems and Applications
Integrating ODK Scan into the community health worker supply chain in Mozambique
Proceedings of the Sixth International Conference on Information and Communication Technologies and Development: Full Papers - Volume 1
Improving form-based data entry with image snippets
Proceedings of Graphics Interface 2013
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In low-resource settings in developing countries, most records are still captured and maintained using paper forms. Despite a recent proliferation of digital data collection systems, paper forms remain a trusted, low-cost and ubiquitous medium that will continue to be utilized in these communities for years to come. However, it can be challenging to aggregate, share, and analyze the data collected using paper forms. This paper presents mScan, a mobile smartphone application that uses computer vision to capture data from paper forms that use a multiple choice or bubble format. The initial mScan implementation targets the task of digitizing paper forms used to record vaccine statistics in rural health centers in Mozambique. We have evaluated the accuracy and performance of mScan under a variety of different environmental conditions, and our results show that mScan is a robust tool that is capable of accurately capturing and digitizing data from paper forms.