The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Top-down and bottom-up cues for scene text recognition
Open accessAbstract
Scene text recognition has gained significant attention from the computer vision community in recent years. Recognizing such text is a challenging problem, even more so than the recognition of scanned documents. In this work, we focus on the problem of recognizing text extracted from street images. We present a framework that exploits both bottom-up and top-down cues. The bottom-up cues are derived from individual character detections from the image. We build a Conditional Random Field model on these detections to jointly model the strength of the detections and the interactions between them. We impose top-down cues obtained from a lexicon-based prior, i.e. language statistics, on the model. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We show significant improvements in accuracies on two challenging public datasets, namely Street View Text (over 15%) and ICDAR 2003 (nearly 10%).
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
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Segmentation and Recognition Using Structure from Motion Point Clouds
Brostow, G. J., Shotton, J., Fauqueur, J., & Cipolla, R. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_5 · Cited by 1,050
Cited by
ASTER: An Attentional Scene Text Recognizer with Flexible Rectification
Shi, B., Yang, M., Wang, X., Lyu, P., Yao, C., & Bai, X. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2848939 · Cited by 906
Scene Text Recognition using Higher Order Language Priors
Mishra, A., Alahari, K., & Jawahar, C. (2012) · Procedings of the British Machine Vision Conference 2012 · doi:10.5244/c.26.127 · Cited by 831
Self-citationTrOCR: Transformer-Based Optical Character Recognition with Pre-trained Models
Li, M., Lv, T., Chen, J., Cui, L., Lu, Y., Florencio, D., Zhang, C., Li, Z., & Wei, F. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26538 · Cited by 573
A robust arbitrary text detection system for natural scene images
Risnumawan, A., Shivakumara, P., Chan, C. S., & Tan, C. L. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.07.008 · Cited by 531
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
- Mishra, A., Alahari, K., & Jawahar, C. V.
- Venue
- 2012 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 2687-2694
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2049951199
- Cited by
- 335
- References
- 5
- FWCI
- 25.07
- Type
- conference paper
- Language
- en
- Open access
- green
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: Top-down and bottom-up cues for scene text recognition. https://app.cochat.ai/papers/10.1109/cvpr.2012.6247990
- The paper
- Mishra, A., Alahari, K., & Jawahar, C. V. (2012). Top-down and bottom-up cues for scene text recognition. 2012 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2012.6247990
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.
