Evaluation of Generic Deep Learning Building Blocks for Segmentation of Nineteenth Century Documents
Although the field of computer vision has grown significantly due to the advent of convolutional neural networks (CNNs), electronic analysis of historical documents has experienced scant research and development attention. Recently, however, computer vision has matured to the point where it can be a...
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| Autores principales: | , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2023
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/88408 |
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| Sumario: | Although the field of computer vision has grown significantly due to the advent of convolutional neural networks (CNNs), electronic analysis of historical documents has experienced scant research and development attention. Recently, however, computer vision has matured to the point where it can be applied to outperform existing, specialized tools for document analysis. This paper demonstrates empirically how state-of-the-art results can be produced by implementing, training, and evaluating generic computer vision models on historical document segmentation tasks. We show the generality of our approach to document analysis and explain how innovation in this domain can arise from combining generic building blocks for computer vision. |
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| ISBN: | 978-1-83768-322-2 |
| DOI: | 10.5772/intechopen.113110 |