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.

Noise tolerant optical character recognition system

Abstract: Disclosed is a method of optical character recognition that first segments a graphical page image into word images. The method obtains a set of features by extracting smaller outlines of the dark regions in the word images, and then further dissecting each of the smaller outlines into small sections called micro-features. Micro-features are simply sections of character outlines, therefore, they can easily be extracted from the outlines of an entire word without any knowledge about character segmentation boundaries. Micro-features are extracted from an outline by finding the local extremities of the outline and then defining a micro-feature between each pair of sequential extremities. Once extracted, the micro-features are compared to micro-features from an ideal character in order to classify a character, and convert it into a character code.

Shape clustering in post optical character recognition processing

Abstract: Techniques for shape clustering and applications in processing various documents, including an output of an optical character recognition (OCR) process.

The State of the Art of Document Image Degradation Modelling

Abstract: The literature on models of document image degradation is reviewed, and open problems are listed. In response to the unpleasant fact that the accuracy of document recognition algorithms falls drastically when image quality degrades even slightly, researchers in the last decade have intensiied their study of explicit, quantitative, parameter-ized models of image defects that occur during printing and scanning. Several models have been proposed, some motivated by the physics of image formation and others by the surface statistics of image distributions. A wide range of techniques for estimating parameters of these models has been explored. These models, in the form of pseudo-random generators of synthetic images, permit, for the rst time, investigations into fundamental properties of concrete image recognition problems including the Bayes error of problems and the asymptotic accuracy and domain of competency of classiier technologies. The use of massive sets of synthetic images, in the construction and testing of high-performance classiiers, has accelerated in the last few years. Open problems include the search for methods for comparing competing models and sound methodologies for the use of synthetic data in engineering.

Off-line handwritten Arabic character segmentation algorithm: ACSA

Abstract: Character segmentation is a necessary preprocessing step for character recognition in many OCR systems. It is an important step because incorrectly segmented characters are unlikely to be recognized correctly. The most difficult case in character segmentation is the cursive script. The scripted nature of Arabic written language poses some high challenges for automatic character segmentation and recognition. In this paper, a new character segmentation algorithm (ACSA) of Arabic scripts is presented. The developed segmentation algorithm yields on the segmentation of isolated handwritten words in perfectly separated characters. It is based on morphological rules, which are constructed at the feature extraction phase. Finally, ACSA is combined with an existing handwritten Arabic character recognition system (RECAM).

A new methodology for gray-scale character segmentation and recognition

Abstract: Generally speaking, through the binarization of gray-scale images, useful information for the segmentation of touching or overlapping characters may be lost. If we analyze gray-scale images, however, specific topographic features and the variation of intensity can be observed in the character boundaries. We believe that such kinds of clues obtained from gray-scale images should be useful for efficient character segmentation. In this paper, we propose a new methodology for character segmentation and recognition which makes the best use of the characteristics of gray-scale images. In the proposed methodology, the character segmentation regions are determined by using projection profiles and topographic features extracted form gray-scale images. Then the nonlinear character segmentation path in each character segmentation region is found by using multistage graph search algorithm. Finally, in order to confirm the character segmentation paths and recognition results, recognition based segmentation method is adopted.