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.

N-tuple features for OCR revisited

Abstract: N-tuple features for optical character recognition have received only scattered attention since the 1960s. Our main purpose is to show that advances in computer technology and computer science compel renewed interest. N-tuple features are useful for printed character classification because they indicate the presence or absence of a given rigid configuration of n black and white pixels in a pattern. Desirable n-tuples fit each pattern of a specified (positive) training set of characters in at least p different shift positions, and fail to fit each pattern of a specified (negative) training set by at least n-q pixels in each shift position. We prove that the problem of finding a distinguishing n-tuple is NP-complete, by examining a natural subproblem with binary strings called the missing configuration problem. The NP-completeness result notwithstanding, distinguishing n-tuples are found automatically in a few seconds on contemporary workstations. We exhibit a practical search algorithm for generating, from a small training set, a collection of n-tuples with low class-conditional correlation and with specified design parameters n, p, and q. The generator, which is available on the Internet, is empirically shown to be effective through a comparison with a benchmark generator. We show experimentally that the design parameters provide a useful tradeoff between distinguishing power and generation time, and also between the conditional probabilities for the positive and negative classes. We explore the feature probabilities obtainable for various dichotomies, and show that the design parameters control the feature probabilities.

Real time text detection and recognition on hand held objects to assist blind people

Abstract: This paper presents camera based system which will help blind person for reading text patterns printed on hand held objects. This is the framework to assist visually impaired persons to read text patterns and convert it into the audio output. To obtain the object from the background and extract the text pattern from that object, the system first proposes the method that will capture the image from the camera and object region is detected. The text which are maximally stable are detected using Maximally Stable External Regions (MSER) feature. A novel algorithm is evaluated on variety of scenes. The detected text is compared with the template and converted into the speech output. The text patterns are localized and binarized using Optical Character Recognition (OCR). The recognized text is converted to an audio output. The speech output is given to the blind user. Experimental results shows the analysis of MSER and OCR for different text patterns. MSER shows that it is robust algorithm for the text detection. Therefore, this paper deals with analysis of detection and recognition of different text patterns on different objects.

On the recognition of Devanagari ancient handwritten characters using SIFT and Gabor features

Abstract: Recognition of Devanagari ancient handwritten character is an important task for resourceful contents' exploitation of the priceless information contained in them. There are numerous Devanagari ancient handwritten documents from fifteenth to the nineteenth century. This paper presents an optical character recognition system for the recognition of Devanagari ancient manuscripts. In this paper, improved recognition results for Devanagari ancient characters have been presented using the scale-invariant feature transform (SIFT) and Gabor filter feature extraction techniques. Support vector machine (SVM) classifier is used for the classification task in this work. For experimental results, a database consisting of 5484 samples of Devanagari characters was collected from various ancient manuscripts placed in libraries and museums. SIFT- and Gabor filter-based features are used to extract the properties of the handwritten Devanagari ancient characters for recognition. Principle component analysis is used to reduce the length of the feature vector for reducing training time of the model and to improve recognition accuracy. Recognition accuracy of 91.39% has been achieved using the proposed system based on tenfold cross-validation technique and poly-SVM classifier.

Hand printed Arabic character recognition system

Abstract: The paper proposes a structural technique for automatic recognition of hand printed Arabic characters. The advantages of this technique are: more efficient for large and complex sets such as Arabic characters; not expensive for feature extraction; and its execution time does not depend on either the font or the size of the characters. The algorithm was implemented on a microcomputer and tested by 10 different users. The recognition rate obtained was about 90%.