A camera phone based currency reader for the visually impaired
Abstract
In this paper we present a camera phone-based currency reader for the visually impaired that can identify the value of U.S. paper currency. Currently, U.S. paper currency can only be identified visually and this situation will continue for a foreseeable future. Our solution harvests the imaging and computational power on camera phones to read these bills. Considering it is impractical for the visually impaired to capture high quality image, our currency reader performs real time processing for each captured frame as the camera approaches the bill. We developed efficient background subtraction and perspective correction algorithms and trained our currency reader using an efficient Ada-boost framework. Our currency reader processes 10 frames/second and achieves a false positive rate of approximately 10 −4. Major smart phone platforms, including Symbian and Windows Mobile, are supported.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The paper contributes the novel dimension of demonstrating competence in addition to confirming prior research showing that self-advocacy involves creating awareness, escalation, and gathering support.
“Thus, we contribute the novel dimension of demonstrating competence in addition to confirming prior research showing that the work of self-advocacy involves creating awareness [15,60], escalation [3,37], and gathering support [3,37].”
From Advocacy as Access Work: How People with Visual Impairments Gain Access to Digital Banking in India (2023) · cites this paper as [37]
Cites
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466 · see citing works
The Ubiquitous Camera: An In-Depth Study of Camera Phone Use
Kindberg, T., Spasojevic, M., Fleck, R., & Sellen, A. (2005) · IEEE Pervasive Computing · doi:10.1109/mprv.2005.42 · Cited by 275 · see citing works
Massof, R. W., Hsu, C. T., Baker, F., et al. (2005) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2004.09.016 · Cited by 92 · see citing works
A wearable face recognition system for individuals with visual impairments
Krishna, S., Little, G., Black, J. A., & Panchanathan, S. (2005) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/1090785.1090806 · Cited by 86 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
VizWiz: nearly real-time answers to visual questions
Bigham, J. P., Jayant, C., Ji, H., Little, G., Miller, A., Miller, R. C., Miller, R., Tatarowicz, A., White, B., White, S., et al. (2010) · Proceedings of the 23nd annual ACM symposium on User interface software and technology · doi:10.1145/1866029.1866080 · Cited by 601 · see citing works
Text Recognition in the Wild: A Survey
Chen, X., Jin, L., Zhu, Y., Luo, C., & Wang, T. (2022) · ACM Computing Surveys · doi:10.1145/3440756 · Cited by 205 · see citing works
Helping visually impaired users properly aim a camera.
Vázquez, M., & Steinfeld, A. (2012) · Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility · doi:10.1145/2384916.2384934 · Cited by 81 · see citing works
Yuan, C. W., Hanrahan, B. V., Lee, S., Rosson, M. B., & Carroll, J. M. (2019) · Universal Access in the Information Society · doi:10.1007/s10209-017-0577-1 · Cited by 38 · see citing works
Advocacy as Access Work: How People with Visual Impairments Gain Access to Digital Banking in India
Kameswaran, V., Y, V., & Marathe, M. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579596 · Cited by 18 · see citing works
- Authors
- Liu, X.
- Affiliations
- University of Maryland, College Park (US)
- Corresponding author
- Liu, Xu
- ORCID
- Liu, Xu
- Venue
- Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility
- Publisher
- ACM
- Published
- 2008-10-13
- Pages
- 305-306
- OpenAlex
- W2062139096
- Cited by
- 74
- References
- 4
- Citations by year
- 2023: 3 · 2022: 3 · 2021: 6 · 2020: 4 · 2019: 7 · 2018: 11 · 2017: 4 · 2016: 5 · 2015: 3 · 2014: 5 · 2013: 4 · 2012: 5
- FWCI
- 3.74
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) (publishedVersion)
- Funders
- Carnegie Mellon University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Currency Recognition and Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- visually impaired people, paper currency recognition, camera phones, mobile vision, background subtraction, perspective correction, AdaBoost, real-time image processing, assistive technology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
