Digital Image Warping
Locating and Recognizing Text in WWW Images
Information Retrieval
ICDAR 2003 Robust Reading Competitions
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Proceedings of the 12th annual ACM international conference on Multimedia
Multimodal fusion using learned text concepts for image categorization
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)
IEEE Transactions on Image Processing
Robust pre-processing techniques for OCR applications on mobile devices
Mobility '09 Proceedings of the 6th International Conference on Mobile Technology, Application & Systems
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Segmentation of Bangla words in scene images
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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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 font types, often affected by perspective distortion, lighting effects, textures or occlusion. Currently there are no datasets publicly available which cover all aspects of natural image OCR. We propose a comprehensive well-annotated configurable dataset for optical character recognition in natural images for the evaluation and comparison of approaches tackling with natural image text OCR. Based on the rich annotations of the proposed NEOCR dataset new and more precise evaluations are now possible, which give more detailed information on where improvements are most required in natural image text OCR.