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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| Hlavní autor: | |
|---|---|
| Další autoři: | , |
| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Vydání: | 1st ed. 2008. |
| Edice: | Studies in Computational Intelligence
90 |
| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Poznámka: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Machine learning in document analysis and recognition, Simone Marinai, Hiromichi Fujisawa (eds.), Berlin, Springer, 2008, 1 vol. (XI-433 p.), Studies in computational intelligence, 978-3-540-76279-9 • Machine Learning in Document Analysis and Recognition, Texte imprimé, 9783540845447 • Machine Learning in Document Analysis and Recognition, Texte imprimé, 9783642095115 • Machine learning in document analysis and recognition, Simone Marinai, Hiromichi Fujisawa (eds.), Berlin, Springer, 2008, 1 vol. (XI-433 p.), Studies in computational intelligence, 978-3-540-76279-9 |
Obsah:
- 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