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Camera-Based Document Analysis and Recognition
Details
This book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2013, held in Washington, DC, USA, in August 2013. The 14 revised full papers presented were carefully selected during two rounds of reviewing and improvement from numerous original submissions. Intended to give a snapshot of the state-of-the-art research in the field of camera based document analysis and recognition, the papers are organized in topical sections on text detection and recognition in scene images and camera-based systems.
Fast track conference proceedings Unique visibility State of the art research Includes supplementary material: sn.pub/extras
Inhalt
Spatially Prioritized and Persistent Text Detection and Decoding.- A Hierarchical Visual Saliency Model for Character Detection.- in Natural Scenes.- A Robust Approach to Extraction of Texts from Camera Captured Images.- Scene Text Detection via Integrated Discrimination of Component Appearance and Consensus.- Accuracy Improvement of Viewpoint-Free Scene Character Recognition by Rotation Angle Estimation.- Sign Detection Based Text Localization in Mobile Device Captured Scene Images.- Font Distribution Observation by Network-Based Analysis.- Book Page Spreads Captured with a Mobile Phone Camera.- A Dataset for Quality Assessment of Camera Captured Document Images.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09783319051666
- Auflage 2014
- Editor Faisal Shafait, Masakazu Iwamura
- Sprache Englisch
- Genre Anwendungs-Software
- Größe H235mm x B155mm x T11mm
- Jahr 2014
- EAN 9783319051666
- Format Kartonierter Einband
- ISBN 3319051660
- Veröffentlichung 10.04.2014
- Titel Camera-Based Document Analysis and Recognition
- Untertitel 5th International Workshop, CBDAR 2013, Washington, DC, USA, August 23, 2013, Revised Selected Papers
- Gewicht 306g
- Herausgeber Springer International Publishing
- Anzahl Seiten 196
- Lesemotiv Verstehen