000 06830nam a22003735i 4500
001 287402
003 MX-SnUAN
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007 cr nn 008mamaa
008 150903s2012 xxk| o |||| 0|eng d
020 _a9781447140726
_99781447140726
024 7 _a10.1007/9781447140726
_2doi
035 _avtls000339710
039 9 _a201509030318
_bVLOAD
_c201404300404
_dVLOAD
_y201402060942
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aTA1637-1638
100 1 _aMärgner, Volker.
_eeditor.
_9316560
245 1 0 _aGuide to OCR for Arabic Scripts /
_cedited by Volker Märgner, Haikal El Abed.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2012.
300 _axx, 590 páginas 327 ilustraciones, 143 ilustraciones en color.
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
500 _aSpringer eBooks
505 0 _aPart I: Pre-Processing -- An Assessment of Arabic Handwriting Recognition Technology -- Layout Analysis of Arabic Script Documents -- A Multi-Stage Approach to Arabic Document Analysis -- Pre-Processing Issues in Arabic OCR -- Segmentation of Ancient Arabic Documents -- Features for HMM-Based Arabic Handwritten Word Recognition Systems -- Part II: Recognition -- Printed Arabic Text Recognition -- Handwritten Arabic Word Recognition Using the IFN/ENIT-Database -- RWTH OCR: A Large Vocabulary Optical Character Recognition System for Arabic Scripts -- Arabic Handwriting Recognition using Bernoulli HMMs -- Handwritten Farsi Words Recognition Using Hidden Markov Models -- Offline Arabic Handwriting Recognition with Multidimensional Recurrent Neural Networks -- Application of Fractal Theory in Farsi/Arabic Document Analysis -- Multi-Stream Markov Models for Arabic Handwriting Recognition -- Towards Distributed Cursive Writing OCR Systems based on the Combination of Complementary Approaches -- Part III: Evaluation -- Data Collection and Annotation for Arabic Document Analysis -- Arabic Handwriting Recognition Competitions -- Benchmarking Strategy for Arabic Screen Rendered Word Recognition -- Part IV: Applications -- A Robust Word Spotting System for Historical Arabic Manuscripts -- Arabic Text recognition using a Script-Independent Methodology: A Unified HMM-based Approach for Machine-print and Handwritten Text -- Arabic Handwriting Recognition Using VDHMM and Over-Segmentation -- Online Arabic Databases and Applications -- Online Arabic Handwritten Words Recognition Based on HMM and Combination of Online and Offline Features -- Part I: Pre-Processing -- An Assessment of Arabic Handwriting Recognition Technology -- Layout Analysis of Arabic Script Documents -- A Multi-Stage Approach to Arabic Document Analysis -- Pre-Processing Issues in Arabic OCR -- Segmentation of Ancient Arabic Documents -- Features for HMM-Based Arabic Handwritten Word Recognition Systems -- Part II: Recognition -- Printed Arabic Text Recognition -- Handwritten Arabic Word Recognition Using the IFN/ENIT-Database -- RWTH OCR: A Large Vocabulary Optical Character Recognition System for Arabic Scripts -- Arabic Handwriting Recognition using Bernoulli HMMs -- Handwritten Farsi Words Recognition Using Hidden Markov Models -- Offline Arabic Handwriting Recognition with Multidimensional Recurrent Neural Networks -- Application of Fractal Theory in Farsi/Arabic Document Analysis -- Multi-Stream Markov Models for Arabic Handwriting Recognition -- Towards Distributed Cursive Writing OCR Systems based on the Combination of Complementary Approaches -- Part III: Evaluation -- Data Collection and Annotation for Arabic Document Analysis -- Arabic Handwriting Recognition Competitions -- Benchmarking Strategy for Arabic Screen Rendered Word Recognition -- Part IV: Applications -- A Robust Word Spotting System for Historical Arabic Manuscripts -- Arabic Text recognition using a Script-Independent Methodology: A Unified HMM-based Approach for Machine-print and Handwritten Text -- Arabic Handwriting Recognition Using VDHMM and Over-Segmentation -- Online Arabic Databases and Applications -- Online Arabic Handwritten Words Recognition Based on HMM and Combination of Online and Offline Features.
520 _aOptical Character Recognition (OCR) is a key technology enabling access to digital text data. This technique is especially valuable for Arabic scripts, for which there has been very little digital access. Arabic script is widely used today. It is estimated that approximately 200 million people use Arabic as a first language, and the Arabic script is shared by an additional 13 languages, making it the second most widespread script in the world. However, Arabic scripts pose unique challenges for OCR systems that cannot be simply adapted from existing Latin character-based processing techniques. This comprehensive Guide to OCR for Arabic Scripts is the first book of its kind, specifically devoted to this emerging field. Presenting state-of-the-art research from an international selection of pre-eminent authorities, the book reviews techniques and algorithms for the recognition of both handwritten and printed Arabic scripts. Many of these techniques can also be applied to other scripts, serving as an inspiration to all groups working in the area of OCR. Topics and features: Contains contributions from the leading researchers in the field With a Foreword by Professor Bente Maegaard of the University of Copenhagen Presents a detailed overview of Arabic character recognition technology, covering a range of different aspects of pre-processing and feature extraction Reviews a broad selection of varying approaches, including HMM-based methods and a recognition system based on multidimensional recurrent neural networks Examines the evaluation of Arabic script recognition systems, discussing data collection and annotation, benchmarking strategies, and handwriting recognition competitions Describes numerous applications of Arabic script recognition technology, from historical Arabic manuscripts to online Arabic recognition This authoritative work is an essential reference for all researchers and graduate students interested in OCR technology and methodology in general, and in Arabic scripts in particular.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aEl Abed, Haikal.
_eeditor.
_9316561
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781447140719
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-4072-6
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c287402
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