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Machine Learning in Document Analysis and Recognition by Simone Marinai (auth.), Prof. Simone Marinai, Prof.

By Simone Marinai (auth.), Prof. Simone Marinai, Prof. Hiromichi Fujisawa (eds.)

The aim of record research and popularity (DAR) is to acknowledge the textual content and graphicalcomponents of a rfile and to extract info. With ?rst papers relationship again to the 1960’s, DAR is a mature yet nonetheless gr- ing research?eld with consolidated and recognized ideas. Optical personality popularity (OCR) engines are probably the most well known pr- ucts of the study during this ?eld, whereas broader DAR ideas are these days studied and utilized to different business and o?ce automation platforms. within the desktop studying group, probably the most widely recognized - seek difficulties addressed in DAR is popularity of unconstrained handwr- ten characters which has been often utilized in the earlier as a benchmark for comparing laptop studying algorithms, specifically supervised classi?ers. despite the fact that, constructing a DAR method is a posh engineering job that consists of the combination of a number of strategies into an natural framework. A reader may well consider that using computing device studying algorithms isn't really approp- ate for different DAR projects than personality acceptance. to the contrary, such algorithms were hugely used for almost the entire projects in DAR. With huge emphasis being dedicated to personality acceptance and note acceptance, different projects comparable to pre-processing, structure research, personality segmentation, and signature veri?cation have additionally bene?ted a lot from computing device studying algorithms.

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The main idea is to generalize a recurrent neuron in a “Generalized Structure Extraction in Printed Documents Using Neural Approaches 27 Recursive Neuron” (GNR). The approach is different from the standard one which focuses on the tree-structure encoding in a fixed input vector. The GRN considers the outputs of the unit for all the vertices which are pointed by the current input vertex. Recurrent Neuron Standard Neuron Complex Recursive Neuron t p n p 1 p 1 Single Pattern p n Sequence of Patterns Tree of Patterns Unstructured Pattern Sequence Complex Structure (Tree, Graph) Structured Pattern Fig.

Both are the results of repeatedly dividing the content of a document into increasingly smaller parts, and are typically represented by means of a tree structure. The difference between them is the criteria adopted for structuring the document content: the layout structure is based on the presentation of the content, while the logical structure is based on the human-perceptible meaning of the content. , text, graphics, pictures) and hierarchical organization on the basis of perceptual D. com 46 D.

2005) 675–679 33. : IFN/ENITdatabase of handwritten arabic words. In: 7th Colloque International Francophone sur l’Ecrit et le Document. (2002) 20 S. Marinai 34. : Ground truth data for document image analysis. In: Symposium on Document Image Understanding and Technology. (2003) 199–205 35. : A full english sentence database for off-line handwriting recognition. In: Int’l Conference on Document Analysis and Recognition. (1999) 705–708 36. : ICDAR 2005 page segmentation competition. In: Int’l Conference on Document Analysis and Recognition.

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