Skeletal similarity based structural performance evaluation for document binarization (2020)
- Authors:
- USP affiliated authors: HIRATA, NINA SUMIKO TOMITA - IME ; SILVA, AUGUSTO CESAR MONTEIRO - IME
- Unidade: IME
- DOI: 10.1109/ICFHR2020.2020.00018
- Assunto: PROCESSAMENTO DE IMAGENS
- Keywords: document binarization; performance evaluation; structural informatio
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2020
- Source:
- Título: Proceedings
- Conference titles: International Conference on Frontiers in Handwriting Recognition - ICFHR
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
SILVA, Augusto Cesar Monteiro e HIRATA, Nina Sumiko Tomita e JIANG, Xiong. Skeletal similarity based structural performance evaluation for document binarization. 2020, Anais.. Piscataway: IEEE, 2020. Disponível em: https://doi.org/10.1109/ICFHR2020.2020.00018. Acesso em: 20 jan. 2026. -
APA
Silva, A. C. M., Hirata, N. S. T., & Jiang, X. (2020). Skeletal similarity based structural performance evaluation for document binarization. In Proceedings. Piscataway: IEEE. doi:10.1109/ICFHR2020.2020.00018 -
NLM
Silva ACM, Hirata NST, Jiang X. Skeletal similarity based structural performance evaluation for document binarization [Internet]. Proceedings. 2020 ;[citado 2026 jan. 20 ] Available from: https://doi.org/10.1109/ICFHR2020.2020.00018 -
Vancouver
Silva ACM, Hirata NST, Jiang X. Skeletal similarity based structural performance evaluation for document binarization [Internet]. Proceedings. 2020 ;[citado 2026 jan. 20 ] Available from: https://doi.org/10.1109/ICFHR2020.2020.00018 - Image operator learning based on local features
- Análise comparativa de abordagens para aprendizado de transformações imagem-a-imagem
- Subexpression and dominant symbol histograms for spatial relation classification in mathematical expressions
- Ferramenta interativa de desenho de redes de regulação gênica
- The use of high resolution images in morphological operator learning
- Morphological operator design from training data
- Automatic labeling of handwritten mathematical symbols via expression matching
- Fast QR code detection in arbitrarily acquired images
- Image operator learning coupled with CNN classification and its application to staff line removal
- Binary image operator design based on stacked generalization
Informações sobre o DOI: 10.1109/ICFHR2020.2020.00018 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
| 3017856.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
