Topic
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.
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Papers
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20 Jun 2011TL;DR: This work trains a similarity expert that learns to classify each pair of characters as equivalent or not and incorporates the equivalence information as constraints in the integer program and builds an optimization criterion out of appearance features and character bigrams.
Abstract: The recognition of text in everyday scenes is made difficult by viewing conditions, unusual fonts, and lack of linguistic context. Most methods integrate a priori appearance information and some sort of hard or soft constraint on the allowable strings. Weinman and Learned-Miller [14] showed that the similarity among characters, as a supplement to the appearance of the characters with respect to a model, could be used to improve scene text recognition. In this work, we make further improvements to scene text recognition by taking a novel approach to the incorporation of similarity. In particular, we train a similarity expert that learns to classify each pair of characters as equivalent or not. After removing logical inconsistencies in an equivalence graph, we formulate the search for the maximum likelihood interpretation of a sign as an integer program. We incorporate the equivalence information as constraints in the integer program and build an optimization criterion out of appearance features and character bigrams. Finally, we take the optimal solution from the integer program, and compare all “nearby” solutions using a probability model for strings derived from search engine queries. We demonstrate word error reductions of more than 30% relative to previous methods on the same data set.
42 citations
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05 Dec 2011TL;DR: A novel video segmenter for automated slide video structure analysis and a weighted DCT (discrete cosines transformation) based text detector are developed for automatic lecture video indexing based on video OCR technology.
Abstract: During the last years, digital lecture libraries and lecture video portals have become more and more popular. However, finding efficient methods for indexing multimedia still remains a challenging task. Since the text displayed in a lecture video is closely related to the lecture content, it provides a valuable source for indexing and retrieving lecture contents. In this paper, we present an approach for automatic lecture video indexing based on video OCR technology. We have developed a novel video segmenter for automated slide video structure analysis and a weighted DCT (discrete cosines transformation) based text detector. A dynamic image constrast/brightness adaption serves the purpose of enhancing the text image quality to make it processible by existing common OCR software. Time-based text occurence information as well as the analyzed text content are further used for indexing. We prove the accuracy of the proposed approach by evaluation.
42 citations
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09 Nov 2011TL;DR: Machine-printed character recognition acquired from license plate using convolutional neural network trained in supervised mode using a gradient descent Backpropagation learning algorithm that enables automated feature extraction is presented.
Abstract: This paper presents machine-printed character recognition acquired from license plate using convolutional neural network (CNN). CNN is a special type of feed-forward multilayer perceptron trained in supervised mode using a gradient descent Backpropagation learning algorithm that enables automated feature extraction. Common methods usually apply a combination of handcrafted feature extractor and trainable classifier. This may result in sub-optimal result and low accuracy. CNN has proved to achieve state-of-the-art results in such tasks such as optical character recognition, generic objects recognition, real-time face detection and pose estimation, speech recognition, license plate recognition etc. CNN combines three architectural concept namely local receptive field, shared weights and subsampling. The combination of these concepts and optimization method resulted in accuracy around 98%. In this paper, the method implemented to increase the performance of character recognition using CNN is proposed and discussed.
42 citations
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03 May 1991TL;DR: One rotationally invariant feature extracted by the system is the number of intercepts between boundary transitions in the image with at least a selected one of a plurality of radii centered at the centroid of the character.
Abstract: A feature-based optical character recognition system, employing a feature-based recognition device such as a neural network or an absolute distance measure device, extracts a set of features from segmented character images in a document, at least some of the extracted features being at least nearly impervious to rotation or skew of the document image, so as to enhance the reliability of the system. One rotationally invariant feature extracted by the system is the number of intercepts between boundary transitions in the image with at least a selected one of a plurality of radii centered at the centroid of the character in the image.
42 citations
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TL;DR: A hidden Markov model-based online handwritten character recognition for Gurmukhi script using a database developed in XML using 5330 GurmUKhi characters is presented.
Abstract: This paper presents a hidden Markov model-based online handwritten character recognition for Gurmukhi script. We discuss a procedure to develop a hidden Markov model database in order to recognize Gurmukhi characters. A test with 60 handwritten samples, where each sample includes 41 Gurmukhi characters, shows a 91.95% recognition rate, and an average recognition speed of 0.112 seconds per stroke. The hidden Markov model database has been developed in XML using 5330 Gurmukhi characters. This work shall be useful to implement a hidden Markov model in online handwriting recognition and its software development.
42 citations