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.

A shape based post processor for Gurmukhi OCR

Abstract: A shape based post processing system for an OCR of Gurmukhi script has been developed. Based on the size and shape of a word, the Punjabi corpora has been split into different partitions. The statistical information of Punjabi language syllable combination, corpora look up and holistic recognition of most commonly occurring words have been combined to design the post processor. An improvement of 3% in recognition rate from 94.35% to 97.34% has been reported on machine printed images using the post processing techniques.

Optimized Gabor filter based feature extraction for character recognition

Abstract: /spl omega/This paper proposed a new feature extraction method for Chinese character recognition by using optimized Gabor filters. Based on the theory of Gabor filters and the statistical information of Chinese character images, a simple but effective method to design Gabor filters was developed. Moreover, to improve the performances for low quality images, we modified the non-linear function used in previous research to regulate the outputs of Gabor filters adaptively. This paper also meliorated the feature extraction method to improve the discriminability of histogram features. Experiments had shown that our method perform excellently for images with noises, backgrounds or stroke distortions and can be applied to printed or handwritten character recognition tasks in low quality greyscale or binary images.

Research on Chinese OCR in Taiwan

Abstract: This paper describes typical research on Chinese optical character recognition in Taiwan. Chinese characters can be represented by a set of basic line segments called strokes. Several approaches to the recognition of handwritten Chinese characters by stroke analysis are described here. A typical optical character recognition (OCR) system consists of four main parts: image preprocessing, feature extraction, radical extraction and matching. Image preprocessing is used to provide the suitable format for data processing. Feature extraction is used to extract stable features from the Chinese character. Radical extraction is used to decompose the Chinese character into radicals. Finally, matching is used to recognize the Chinese character. The reasons for using strokes as the features for Chinese character recognition are the following. First, all Chinese characters can be represented by a combination of strokes. Second, the algorithms developed under the concept of strokes do not have to be modified when the number of characters increases. Therefore, the algorithms described in this paper are suitable for recognizing large sets of Chinese characters.

Automatic computer recognition of printed music

Abstract: This paper provides an overview to the implementation of Lemon, a complete optical music recognition system. Among the techniques employed by the implementation are: template matching, the Hough transform, line adjacency graphs, character profiles, and graph grammars. Experimental results, including comparisons with commercial systems, are provided.