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Subject: search.filters.subject.feature extraction

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    OFFLINE HANDWRITTEN MATHEMATICAL EXPRESSION RECOGNITION
    (International Journal of Innovative Research in Computer and Communication Engineering, 2016-01) Padmapriya R; Karpagavalli S
    Recognition of handwritten mathematical expressions is helpful in writing technical documents as well as useful in converting handwritten documents with mathematical equations into electronic format. Symbol recognition in mathematical expressions is a complex task due to large character set and writer variability in size and style of symbols. In this work, mathematical expression recognition task carried out in different phases which include data collection, preprocessing, segmentation, feature extraction, symbol classification as well as mathematical expression. A set of 50 simple algebraic expressions written by 10 writers, each equation with 10 to 15 symbols converting 23 unique symbols are collected. The expressions are scanned and converted into image files. The images are preprocessed to remove noises, normalize the size and enhance. The symbols in each equation is segmented and features like, zonal, structural, skeleton based, directional are extracted. Multilayer Perceptron (MLP) and Support Vector Machine (SVM) classifiers are used to classify the symbols. The accuracy of symbol classification and whole algebraic expression recognition is analyzed. An interface to automatic mathematical expression recognition is developed with effective classifier.
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    AUTOMATIC SPEECH RECOGNITION: ARCHITECTURE, METHODOLOGIES, CHALLENGES - A REVIEW
    (International Journal of Advanced Research in Computer Science, 2011-11) Karpagavalli S; Deepika R; Kokila P; Usha Rani K; Chandra E
    For more than three decades, a great amount of research was carried out on various aspects of speech signal processing and its applications. Highly successful application of speech processing is Automatic Speech Recognition (ASR). Early attempts to ASR consisted of making deterministic models of whole words in a small vocabulary and recognizing a given speech utterance as the word whose model comes closest to it. The introduction of Hidden Morkov Models (HMMs) in the early 1980 provided much more powerful tool for speech recognition. And the recognition can be done for continuous speech using large vocabulary, in a speaker independent manner. Today many products have been developed that successfully utilize ASR for communication between human and machines. Performance of speech recognition applications deteriorates in the presence of reverberation and even low levels of ambient noise. Robustness to noise, reverberation and characteristics of the transducer is still an unsolved problem that makes the research in the area of speech recognition still very active. A detailed study on ASR carried out and presented in this paper that covers the basic model of speech recognition, applications