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.

Learning-based approach for license plate recognition

Abstract: Presents a learning-based approach for the construction of a license-plate recognition system. The system consists of three modules. They are, respectively, the car detection module, the license-plate segmentation module and the recognition module. The car detection module detects a car in a given image sequence obtained from a camera with a simple color-based approach. The segmentation module extracts the license plate in the detected car image using neural networks as filters for analyzing the color and texture properties of the license plate. The recognition module then reads the characters on the detected license plate with a support vector machine (SVM)-based character recognizer. The system has been tested with 1000 video sequences obtained from toll-gates, parking lots, etc., and has shown the following performances on average: car detection rate 100%, segmentation rate 97.5%, and character recognition rate about 97.2%.

Optical character recognition — a survey

Abstract: In order to highlight the interesting problems and actual results on the state of the art in optical character recognition (OCR), this paper describes and compares preprocessing, feature extraction and postprocessing techniques for commercial reading machines. Problems related to handwritten and printed character recognition are pointed out, and the functions and operations of the major components of an OCR system are described. Historical background on the development of character recognition is briefly given and the working of an optical scanner is explained. The specifications of several recognition systems that are commercially available are reported and compared.

ICDAR 2005 Arabic handwriting recognition competition

Abstract: This paper describes the Arabic handwriting recognition competition held at ICDAR 2007. This second competition (the first was at ICDAR 2005) again uses the IFN/ENIT-database with Arabic handwritten Tunisian town names. Today, more than 54 research groups from universities, research centers, and industry are working with this database worldwide. This year, 8 groups with 14 systems are participating in the competition. The systems were tested on known data and on two datasets which are unknown to the participants. The systems are compared on the most important characteristic, the recognition rate. Additionally, the relative speed of the different systems were compared. A short description of the participating groups, their systems, and the results achieved are finally presented.

Off-line, handwritten numeral recognition by perturbation method

Abstract: This paper presents a new approach to off-line, handwritten numeral recognition. From the concept of perturbation due to writing habits and instruments, we propose a recognition method which is able to account for a variety of distortions due to eccentric handwriting. We tested our method on two worldwide standard databases of isolated numerals, namely CEDAR and NIST, and obtained 99.09 percent and 99.54 percent correct recognition rates at no-rejection level respectively. The latter result was obtained by testing on more than 170000 numerals.

Document image binarization based on texture features

Abstract: Binarization has been difficult for document images with poor contrast, strong noise, complex patterns, and/or variable modalities in gray-scale histograms. We developed a texture feature based thresholding algorithm to address this problem. Our algorithm consists of three steps: 1) candidate thresholds are produced through iterative use of Otsu's algorithm (1978); 2) texture features associated with each candidate threshold are extracted from the run-length histogram of the accordingly binarized image; 3) the optimal threshold is selected so that desirable document texture features are preserved. Experiments with 9,000 machine printed address blocks from an unconstrained US mail stream demonstrated that over 99.6 percent of the images were successfully binarized by the new thresholding method, appreciably better than those obtained by typical existing thresholding techniques. Also, a system run with 500 troublesome mail address blocks showed that an 8.1 percent higher character recognition rate was achieved with our algorithm as compared with Otsu's algorithm.