Definition and Evaluation of the NEOCR Dataset for Natural-Image Text Recognition

Please always quote using this URN: urn:nbn:de:bvb:29-opus-28598
  • Recently growing attention has been paid to recognizing text in natural images. Natural image text OCR is far more complex than OCR in scanned documents. Text in real world environments appears in arbitrary colors, font sizes and typefaces, often affected by perspective distortion, lighting effects, textures or occlusion. Currently there is no dataset publicly available that covers all aspects of natural image OCR. A comprehensive well-annotated configurable dataset for optical character recognition in natural images is defined and created for the evaluation and comparison of approaches tackling with natural-image text OCR. Furthermore, current open source and commercial OCR tools have beenRecently growing attention has been paid to recognizing text in natural images. Natural image text OCR is far more complex than OCR in scanned documents. Text in real world environments appears in arbitrary colors, font sizes and typefaces, often affected by perspective distortion, lighting effects, textures or occlusion. Currently there is no dataset publicly available that covers all aspects of natural image OCR. A comprehensive well-annotated configurable dataset for optical character recognition in natural images is defined and created for the evaluation and comparison of approaches tackling with natural-image text OCR. Furthermore, current open source and commercial OCR tools have been analyzed in various test scenarios using the proposed NEOCR dataset. Based on the results further steps to be addressed by the OCR community are concluded towards all-embracing natural-image text recognition.show moreshow less

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Metadaten
Author:Robert Nagy, Anders Dicker, Klaus Meyer-Wegener
Persistent identifiers - URN:urn:nbn:de:bvb:29-opus-28598
Series (Volume number):Technical reports / Department Informatik (CS-2011-07)
Document Type:Report
Language:English
Year of publication:2011
Publishing Institution:Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Release Date:2011/09/28
Tag:Szenentext; natürliche Fotoaufnahmen
dataset; evaluation; latin characters; ocr; scene text recognition
SWD-Keyword:Optische Zeichenerkennung; Datensatz; Photographie; Evaluation
Institutes:Technische Fakultät / Technische Fakultät -ohne weitere Spezifikation-
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
open_access (DINI-Set):open_access
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