The DOI matches its Crossref record.
Word spotting: a new approach to indexing handwriting
Abstract
There are many historical manuscripts written in a single hand which it would be useful to index. Examples include the W.B. DuBois collection at the University of Massachusetts and the early Presidential libraries at the Library of Congress. Since Optical Character Recognition (OCR) does not work well on handwriting, an alternative scheme based on matching the images of the words is proposed for indexing such texts. The current paper deals with the matching aspects of this process. Two different techniques for matching words are discussed. The first method matches words assuming that the transformation between the words may be modelled by a translation (shift). The second method matches words assuming that the transformation between the words may be modelled by an affine transform. Experiments are shown demonstrating the feasibility of the approach for indexing handwriting. The method should also be applicable to retrieving previously stored material from personal digital assistants (PDAs).
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Reading text in the wild with convolutional neural networks
Jaderberg, M., Simonyan, K., Vedaldi, A., & Zisserman, A. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-015-0823-z · Cited by 1,191
Word Spotting and Recognition with Embedded Attributes
Almazan, J., Gordo, A., Fornes, A., & Valveny, E. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2339814 · Cited by 517
Metadata
- Authors
- Manmatha, R., Han, C., & Riseman, E. M.
- Venue
- Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 1996
- Pages
- 631-637
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1932188282
- Cited by
- 241
- References
- 0
- FWCI
- 2.26
- Type
- conference paper
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Word spotting: a new approach to indexing handwriting. https://app.cochat.ai/papers/10.1109/cvpr.1996.517139
- The paper
- Manmatha, R., Han, C., & Riseman, E. M. (1996). Word spotting: a new approach to indexing handwriting. Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.1996.517139
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
