rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine Learning in Document Analysis and Recognition
  name:
    name part: Marinai, Simone.
    role:
      role term: editor.
  name:
    name part: Fujisawa, Hiromichi.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2008
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 434 p. 142 illus. online resource.
  abstract: 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 Recognition (OCR) engines are some of the most widely recognized pr- ucts of the research in this ?eld, while broader DAR techniques are nowadays studied and applied to other industrial and o?ce automation systems. In the machine learning community, one of the most widely known - search problems addressed in DAR is recognition of unconstrained handwr- ten characters which has been frequently used in the past as a benchmark for evaluating machine learning algorithms, especially supervised classi?ers. However, developing a DAR system is a complex engineering task that involves the integration of multiple techniques into an organic framework. A reader may feel that the use of machine learning algorithms is not approp- ate for other DAR tasks than character recognition. On the contrary, such algorithms have been massively used for nearly all the tasks in DAR. With large emphasis being devoted to character recognition and word recognition, other tasks such as pre-processing, layout analysis, character segmentation, and signature veri?cation have also bene?ted much from machine learning algorithms.
  table of contents: 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.
  note: edited by Simone Marinai, Hiromichi Fujisawa.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Applied mathematics
  subject:
    topic: Engineering mathematics
  subject:
    topic: Engineering
  subject:
    topic: Appl.Mathematics/Computational Methods of Engineering
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: TA329-348
  classification: TA640-643
  classification: 519
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Computational Intelligence, 90
  identifier: 9783540762805
  identifier: http://dx.doi.org/10.1007/978-3-540-76280-5
  record info:
    record creation date: 100301
    record change date: 20151204183719.0
    record identifier: 978-3-540-76280-5