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.

Urdu Nastaleeq Optical Character Recognition

Abstract: This paper discusses the Urdu script characteristics, Urdu Nastaleeq and a simple but a novel and robust technique to recognize the printed Urdu script without a lexicon. Urdu being a family of Arabic script is cursive and complex script in its nature, the main complexity of Urdu compound/connected text is not its connections but the forms/shapes the characters change when it is placed at initial, middle or at the end of a word. The characters recognition technique presented here is using the inherited complexity of Urdu script to solve the problem. A word is scanned and analyzed for the level of its complexity, the point where the level of complexity changes is marked for a character, segmented and feeded to Neural Networks. A prototype of the system has been tested on Urdu text and currently achieves 93.4% accuracy on the average.

Fish Classification Based on Robust Features Extraction From Color Signature Using Back-Propagation Classifier

Abstract: Problem statement: Image recognition was a challenging problem resear chers had been research into this area for so long especially in t he recent years, due to distortion, noise, segmenta tion errors, overlap and occlusion of objects in digital images. In our study, there are many fields concer n with pattern recognition, for example, fingerprint verification, face recognition, iris discrimination , chromosome shape discrimination, optical character recognition, texture discrimination and speech recognition, the subject of pattern recognition app ears. A system for recognizing isolated pattern of interest may be as an approach for dealing with suc h application. Scientists and engineers with interests in image processing and pattern recogniti on have developed various approaches to deal with digital image recognition problems such as, neural network, contour matching and statistics. Approach: In this study, our aim was to recognize an isolate d pattern of interest (fish) in the image based robust features extraction. Where depend on c olor signatures that are extracted by RGB color space, color histogram and gray level co-occurrence matrix. Results: We presented a system prototype for dealing with such problem. The system started b y acquiring an image containing pattern of fish, then the image segmentation was performed relying on color signature. Our system has been applied on 20 different fish families, each family has a di fferent number of fish types and our sample consist s of distinct 610 of fish images. These images are di vided into two datasets: 400 training images and 21 0 testing images. An overall accuracy was obtained us ing back-propagation classifier was 84% on the test dataset used. Conclusion: We developed a classifier for fish images recognit ion. We efficiently have chosen an image segmentation method to fit our demands. Our classifier successfully design and implement a decision which performed efficiently wi thout any problems. Eventually, the classifier was able to categorize the given fish into its cluster and categorize the clustered fish into its poison o r non- poison fish and categorizes the poison and non-pois on fish into its family.

Methods and apparatus for using optical character recognition to provide augmented reality

Abstract: A processing system uses optical character recognition (OCR) to provide augmented reality (AR). The processing system automatically determines, based on video of a scene, whether the scene includes a predetermined AR target. In response to determining that the scene includes the AR target, the processing system automatically retrieves an OCR zone definition associated with the AR target. The OCR zone definition identifies an OCR zone. The processing system automatically uses OCR to extract text from the OCR zone. The processing system uses results of the OCR to obtain AR content which corresponds to the text from the OCR zone. The processing system automatically causes that AR content to be presented in conjunction with the scene. Other embodiments are described and claimed.

PC based number plate recognition system

Abstract: A PC based number plate recognition system is presented Digital gray-level images of cars are thresholded using the Niblack algorithm, which was found to outperform all binarization techniques previously used in similar systems A simple yet highly effective rule-based algorithm detects the position and size of number plates Characters are segmented from the thresholded plate using blob-colouring, and passed as 15/spl times/15 pixel bitmaps to a neural network based optical character recognition (OCR) system A novel dimension reduction technique reduces the neural network inputs from 225 to 50 features Six small networks in parallel are used, each recognising six characters The system can recognize single and double line plates under varying lighting conditions and slight rotation Successful recognition of complete registration plates is about 861%

A system to read names and addresses on tax forms

Abstract: The reading of names and addresses is one of the most complex tasks in automated forms processing. This paper describes an integrated real-time system to read names and addresses on tax forms of the U.S. Internal Revenue Service. The Name and Address Block Reader (NABR) system accepts both machine-printed and hand-printed address block images as input. The application software has two major steps: document analysis (connected component analysis, address block extraction, label detection, hand-print/machine-print discrimination) and document recognition. Document recognition has two nonidentical streams for machine-print and hand-print: the key steps are address parsing, character recognition, word recognition, and postal database lookup. (ZIP+4 and City-State-ZIP files.) System output is a packet containing the results of recognition together with database access status file. Real-time throughput (8500 forms/h) is achieved by employing a loosely coupled multiprocessing architecture where successive input images are distributed to available address recognition processors. The functional architecture, software design, system architecture, and the hardware implementation are described. Performance evaluation on machine-printed and handwritten addresses are presented.