Machine Learning in Document Analysis and Recognition
The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphicalcomponents of a document and to extract information. With ?rst papers dating back to the 1960’s, DAR is a mature but still gr- ing research?eld with consolidated and known techniques. Optical Character Rec...
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| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2008.
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| Colección: | Studies in Computational Intelligence ;
90. Springer eBooks. |
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b3303526x*spi |
Tabla de Contenidos:
- to Document Analysis and Recognition
- Structure Extraction in Printed Documents Using Neural Approaches
- Machine Learning for Reading Order Detection in Document Image Understanding
- Decision-Based Specification and Comparison of Table Recognition Algorithms
- Machine Learning for Digital Document Processing: from Layout Analysis to Metadata Extraction
- Classification and Learning Methods for Character Recognition: Advances and Remaining Problems
- Combining Classifiers with Informational Confidence
- Self-Organizing Maps for Clustering in Document Image Analysis
- Adaptive and Interactive Approaches to Document Analysis
- Cursive Character Segmentation Using Neural Network Techniques
- Multiple Hypotheses Document Analysis
- Learning Matching Score Dependencies for Classifier Combination
- Perturbation Models for Generating Synthetic Training Data in Handwriting Recognition
- Review of Classifier Combination Methods
- Machine Learning for Signature Verification
- Off-line Writer Identification and Verification Using Gaussian Mixture Models.
