Titlebook: Machine Learning in Document Analysis and Recognition; Simone Marinai,Hiromichi Fujisawa Book 2008 Springer-Verlag Berlin Heidelberg 2008

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Perturbation Models for Generating Synthetic Training Data in Handwriting Recognition,ld, the authors‘ main results regarding this research area are presented and discussed, including a perturbation model for the generation of synthetic text lines from existing cursively handwritten lines of text produced by human writers. The goal of synthetic text line generation is to improve the

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Machine Learning for Signature Verification, samples. From the viewpoint of automating the task it can be viewed as one that involves machine learning from a population of signatures. There are two types of learning tasks to be accomplished: person-independent (or general) learning and person-dependent (or special) learning. General learning

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Machine Learning for Digital Document Processing: from Layout Analysis to Metadata Extraction,ssing from acquisition to indexing, from categorization to storing and retrieval..The prototypical version of the system DOMINUS is presented, whose main characteristic is the use of a Machine Learning Server, a suite of different inductive learning methods and systems, among which the more suitable

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Adaptive and Interactive Approaches to Document Analysis,ncepts of unsupervised learning and adaptation. Human interaction is often more effective interspersed with algorithmic processes than only before or after the automated parts of the process. We develop a taxonomy for interaction during training and testing, and show how either human-initiated and m

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Multiple Hypotheses Document Analysis,rs that are learned from samples in advance. The second part of the solution, which relies on a hypothesis-driven approach for the segmentation of the numerical character line will also be presented. As a test case, these solutions were applied to the Japanese postal address recognition system. They