Machine Learning in Document Analysis and Recognition

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Marinai, Simone (Επιμελητής έκδοσης), Fujisawa, Hiromichi (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Σειρά: Studies in Computational Intelligence, 90
• Θέματα: Engineering.; Artificial intelligence.; Applied mathematics.; Engineering mathematics.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

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...

Πίνακας περιεχομένων:

• 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.