Definition and Evaluation of the NEOCR Dataset for Natural-Image Text Recognition - Nagy, Robert; Meyer-Wegener, Klaus; Dicker, Anders (2011)
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 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 recognitio
UnivIS-Import:2016-06-30:Pub.2011.tech.IMMD.IMMD6.defini
Keywords: ocr; optical character recognition; scene text recognition; evaluation; dataset

NEOCR: A Configurable Dataset for Natural Image Text Recognition - Nagy, Robert; Dicker, Anders; Meyer-Wegener, Klaus (Camera-Based Document Analysis and Recognition, 2011)
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD6.neocra

Towards Extensible Automatic Image Annotation with the Bag-of-Words Approach - Nagy, Robert; Meyer-Wegener, Klaus (Proceedings of the International Workshop on Very-Large-Scale Multimedia Corpus, Mining and Retrieval, 2010)
Visual-word-based image categorization has proven to be very effective in several publications and contests. Recently, various approaches have been proposed to address the need for scalability and computational performance of classification based on Bag of Words. Despite these efforts, extensibility still remains an issue. Classifiers and histograms of visual words are both heavily dependent on an immutable general visual vocabulary created during the training step based on training images. Adding a new category that is insufficiently represented by the visual words in the vocabulary requires recreation of the visual vocabulary, complete recomputation of histograms and retraining of classifiers. When adding a new category, current approaches need to fully rebuild the whole recognition system. We address the problem of extensibility by combining class-specific vocabularies with outlier visual words. Classification is achieved by computing a scoring function for each class-specific vocabulary and selecting the highest score value. We show first results of our highly parallelizable and distributable approach on the Caltech 256 dataset.
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD6.toward
Keywords: object recognition; bag of words; extensibility