The DOI matches its Crossref record.
Reading handwritten digits: a ZIP code recognition system
Abstract
A neural network algorithm-based system that reads handwritten ZIP codes appearing on real US mail is described. The system uses a recognition-based segmenter, that is a hybrid of connected-components analysis (CCA), vertical cuts, and a neural network recognizer. Connected components that are single digits are handled by CCA. CCs that are combined or dissected digits are handled by the vertical-cut segmenter. The four main stages of processing are preprocessing, in which noise is removed and the digits are deslanted, CCA segmentation and recognition, vertical-cut-point estimation and segmentation, and directly lookup. The system was trained and tested on approximately 10000 images, five- and nine-digit ZIP code fields taken from real mail.>
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
SDNN's have been used in combination with HMM's or other elastic matching methods for handwritten word recognition.
“SDNN's have been used in combination with HMM's or other elastic matching methods for handwritten word recognition [91], [92].”
From Gradient-based learning applied to document recognition (1998) · cites this paper as [92]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 3
- 10.1109/IJCNN.1992.227175 · DOI · Paper page
- lecolinet, 1989, A New System for Automatic Segmentation and Recognition of Unconstrained Handwritten ZIP Codes, Proc Sixth Scandinavian Conf Image Analysis, 1, 585
- 10.1109/ICPR.1990.119325 · DOI
Cited by
Sorted by citations
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Self-citation
Metadata
- Authors
- Matan, O., Baird, H. S., Bromley, J., Burges, C. J. C., Denker, J. S., Jackel, L. D., Le Cun, Y., Pednault, E. P. D., Satterfield, W. D., Stenard, C. E., & Thompson, T. J.
- Affiliations
- AT&T (United States) (US)
- Venue
- Computer
- Published
- 1992-07
- Volume
- 25
- Issue
- 7
- Pages
- 59-63
- ISSN
- 0018-9162 (ISSN-L)
- OpenAlex
- W1994065256
- Cited by
- 48
- References
- 0
- Citations by year
- 2023: 1 · 2022: 3 · 2021: 1 · 2020: 1 · 2019: 1 · 2018: 3 · 2017: 1 · 2016: 2 · 2015: 3 · 2014: 2 · 2013: 2
- FWCI
- 3.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer (publishedVersion)
- Open Research Online (The Open University) (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Vehicle License Plate Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- handwritten digit recognition, connected component analysis, neural network classifier
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
