Topic
Intelligent word recognition
About: Intelligent word recognition is a research topic. Over the lifetime, 2480 publications have been published within this topic receiving 45813 citations.
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TL;DR: The whole word pattern-matching principles used in these machines are described, and it is shown how these principles can be extended to deal with continuously spoken sequences of words.
Abstract: Machines that recognize isolated words from a small, predefined vocabulary have been commercially available for many years. The whole word pattern-matching principles used in these machines are described, and it is shown how these principles can be extended to deal with continuously spoken sequences of words. Details are given of the resulting connected word recognition algorithm which has several novel features and potentially useful extensions. The algorithm has already been implemented in real-time hardware, which will be used to explore the full potential and limitations of the method in many different applications.
51 citations
TL;DR: A two pass contextual processing algorithm limited to the word level using a partial dictionary with an augmented dictionary approach, modified Viterbi algorithm and some heuristics based on pragmatic features is presented.
51 citations
15 Oct 2003
TL;DR: It has been found from experimental results that success rate is approximately 98% for isolated characters and 96% for continuous character in this OCR system for Bengali character.
Abstract: This paper is concerned with a complete optical character recognition (OCR) system for Bengali character. Recognition is done for both isolated and continuous printed multi font Bengali characters. Preprocessing steps includes segmentation in various levels, noise removal and scaling. Freeman chain code has been calculated from scaled character which is further processed to obtain a discriminating set of feature vectors for the recognizer. The unknown samples are classified using feed forward neural network based recognition scheme. It has been found from experimental results that success rate is approximately 98% for isolated characters and 96% for continuous character.
51 citations
19 Mar 2010
TL;DR: This paper proposes a recognition model for English handwritten (lowercase, uppercase and letter) character recognition that uses Freeman chain code (FCC) as the representation technique of an image character.
Abstract: This paper proposes a recognition model for English handwritten (lowercase, uppercase and letter) character recognition that uses Freeman chain code (FCC) as the representation technique of an image character. Chain code representation gives the boundary of a character image in which the codes represent the direction of where is the location of the next pixel. An FCC method that uses 8-neighbourhood that starts from direction labelled as 1 to 8 is used. Randomized algorithm is used to generate the FCC. After that, features vector is built. The criteria of features to input the classification is the chain code that converted to 64 features. Support vector machine (SVM) is chosen for the classification step. NIST Databases are used as the data in the experiment. Our test results show that by applying the proposed model, we reached a relatively high accuracy for the problem of English handwritten recognition.
51 citations
TL;DR: This paper attempts to clarify the fundamentals of character recognition, highlighting the processes involved in using a standard database for ‘learning’ character sets and also the standards and constraints imposed by researchers concerning the constitution of a valid character.
51 citations