Optical character recognition

About: Optical character recognition is a research topic. Over the lifetime, 7342 publications have been published within this topic receiving 158193 citations. The topic is also known as: OCR & optical character reader.

Region-Based Discriminative Feature Pooling for Scene Text Recognition

Abstract: We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition. Many existing methods rely on Histogram of Oriented Gradient (HOG) or part-based models, which do not span the feature space well for characters in natural scene images, especially given large variation in fonts with cluttered backgrounds. In this work, we propose a discriminative feature pooling method that automatically learns the most informative sub-regions of each scene character within a multi-class classification framework, whereas each sub-region seamlessly integrates a set of low-level image features through integral images. The proposed feature representation is compact, computationally efficient, and able to effectively model distinctive spatial structures of each individual character class. Extensive experiments conducted on challenging datasets (Chars74K, ICDAR'03, ICDAR'11, SVT) show that our method significantly outperforms existing methods on scene character classification and scene text recognition tasks.

Identification of text on colored book and journal covers

Abstract: An approach to automatic text location and identification of colored book and journal covers is proposed. To reduce the amount of small variations in color, a clustering algorithm is applied in a preprocessing step. Two methods have been developed for extracting text hypotheses. One is based on a top-down analysis using successive splitting of image regions. The other is a bottom-up region growing algorithm. The results of both methods are combined to robustly distinguish between text and non-text elements. Text elements are binarized using automatically extracted information about text color. The binarized text regions can be used as input for a conventional OCR module. Results are shown for parts of book and journal covers of different complexity. The proposed method is not restricted to cover pages, but can be applied to the extraction of text from other types of color images as well.

Content-based retrieval in multimedia imaging

Abstract: Content-based retrieval is founded on neural networks, this technology allows automatic filing of images and a wide range of possible queries of the resulting database. This is in contrast to methods such as entering SQL keys manually for each image as it is filed and later correctly re-entering those keys to retrieve the same image. An SQL-based approach does not take into account information that is hard to describe with text, such as sounds and images. Neural networks can be trained to translate `noisy' or chaotic image data into simpler, more reliable feature sets. By converting the images into the level of abstraction necessary for symbolic processing, standard database indexing methods can then be applied, or used in layers of associative database neural networks directly.

Bangla/English script identification based on analysis of connected component profiles

Abstract: Script identification is required for a multilingual OCR system. In this paper, we present a novel and efficient technique for Bangla/English script identification with applications to the destination address block of Bangladesh envelope images. The proposed approach is based upon the analysis of connected component profiles extracted from the destination address block images, however, it does not place any emphasis on the information provided by individual characters themselves and does not require any character/line segmentation. Experimental results demonstrate that the proposed technique is capable of identifying Bangla/English scripts on the real Bangladesh postal images.

OCRSpell: an interactive spelling correction system for OCR errors in text

Abstract: In this paper, we describe a spelling correction system designed specifically for OCR-generated text that selects candidate words through the use of information gathered from multiple knowledge sources. This system for text correction is based on static and dynamic device mappings, approximate string matching, and n-gram analysis. Our statistically based, Bayesian system incorporates a learning feature that collects confusion information at the collection and document levels. An evaluation of the new system is presented as well.