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Your search keyword '"Fornes, Alicia"' showing total 9 results
Start Over Author "Fornes, Alicia" Topic handwriting recognition
9 results on '"Fornes, Alicia"'

1. Hybrid Grammar Language Model for Handwritten Historical Documents Recognition

Author

Cirera, Núria, Fornés, Alícia, Frinken, Volkmar, Lladós, Josep, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sanches, João M., editor, Micó, Luisa, editor, and Cardoso, Jaime S., editor

Published
2013
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2. Bidirectional Language Model for Handwriting Recognition

Author

Frinken, Volkmar, Fornés, Alicia, Lladós, Josep, Ogier, Jean-Marc, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gimel’farb, Georgy, editor, Hancock, Edwin, editor, Imiya, Atsushi, editor, Kuijper, Arjan, editor, Kudo, Mineichi, editor, Omachi, Shinichiro, editor, Windeatt, Terry, editor, and Yamada, Keiji, editor

Published
2012
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3. Content and Style Aware Generation of Text-Line Images for Handwriting Recognition.

Author

Kang, Lei, Riba, Pau, Rusinol, Marcal, Fornes, Alicia, and Villegas, Mauricio

Subjects
IMAGE recognition (Computer vision), TEXT recognition, GENERATIVE adversarial networks
Abstract

Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually labeled training data. To alleviate this labor-consuming problem, synthetic data produced with TrueType fonts has been often used in the training loop to gain volume and augment the handwriting style variability. However, there is a significant style bias between synthetic and real data which hinders the improvement of recognition performance. To deal with such limitations, we propose a generative method for handwritten text-line images, which is conditioned on both visual appearance and textual content. Our method is able to produce long text-line samples with diverse handwriting styles. Once properly trained, our method can also be adapted to new target data by only accessing unlabeled text-line images to mimic handwritten styles and produce images with any textual content. Extensive experiments have been done on making use of the generated samples to boost Handwritten Text Recognition performance. Both qualitative and quantitative results demonstrate that the proposed approach outperforms the current state of the art. [ABSTRACT FROM AUTHOR]

Published
2022
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4. Word Spotting and Recognition with Embedded Attributes.

Author

Almazan, Jon, Gordo, Albert, Fornes, Alicia, and Valveny, Ernest

Subjects
WORD recognition, DOCUMENT imaging systems, LEXICON, DIGITIZATION of archival materials, COMPUTER vision
Abstract

This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks. [ABSTRACT FROM AUTHOR]

Published
2014
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5. A New Optimization Approach to Improve an Ensemble Learning Model: Application to Persian/Arabic Handwritten Character Recognition

Author

Motamedisedeh, Omid, Zagia, Faranak, Alaei, Alireza, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Coustaty, Mickael, editor, and Fornés, Alicia, editor

Published
2023
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6. CNN-Based Ruled Line Removal in Handwritten Documents

Author

Gold, Christian, Zesch, Torsten, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Porwal, Utkarsh, editor, Fornés, Alicia, editor, and Shafait, Faisal, editor

Published
2022
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7. Handwriting Recognition and Automatic Scoring for Descriptive Answers in Japanese Language Tests

Author

Nguyen, Hung Tuan, Nguyen, Cuong Tuan, Oka, Haruki, Ishioka, Tsunenori, Nakagawa, Masaki, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Porwal, Utkarsh, editor, Fornés, Alicia, editor, and Shafait, Faisal, editor

Published
2022
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8. Active Transfer Learning for Handwriting Recognition

Author

Burdett, Eric, Fujimoto, Stanley, Brown, Timothy, Shurtz, Ammon, Segrera, Daniel, Sorenson, Lawry, Clement, Mark, Price, Joseph, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Porwal, Utkarsh, editor, Fornés, Alicia, editor, and Shafait, Faisal, editor

Published
2022
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9. Date Recognition in Historical Parish Records

Author

Piqueras, Laura Cabello, Fierro, Constanza, Lotz, Jonas F., Rust, Phillip, Rommedahl, Joen, Due, Jeppe Klok, Igel, Christian, Elliott, Desmond, Pedersen, Carsten B., Salazar, Israfel, Søgaard, Anders, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Porwal, Utkarsh, editor, Fornés, Alicia, editor, and Shafait, Faisal, editor

Published
2022
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