This paper presents the extension of an existing visionbased gesture recognition system using Hidden Markov Models (HMMs). Several improvements have been carried out in order to increase the capabilities and the functionality of the system. These improvements include positionindependent recognition, rejection of unknown gestures, and continuous online recognition of spontaneous gestures. We show that especially the latter requirement is highly complicated and demanding, if we allow the user to move in front of the camera without any restrictions and to perform the gestures spontaneously at any arbitrary moment. We present novel solutions to this problem by modifying the HMM-based decoding process and by introducing online feature extraction and evaluation methods.
This paper presents the design of a system for the processing and recognition of online handwritten mathematical formulas. The Hidden Markov Model (HMM) based system is trained and evaluated with a writer dependent database consisting of 100 formulas for the training and an additional set of 30 formulas for the test. With the introduction of some constraints, it is possible to obtain high recognition rates up to 97.7%, and to transform the transcriptions of the formulas into T E X-syntax in order to achieve a convenient visualization of the results.
This paper presents the design of a system for the processing and recognition of online handwritten mathematical formulas. The Hidden Markov Model (HMM) based system is trained and evaluated with a writer dependent database consisting of 100 formulas for the training and an additional set of 30 formulas for the test. With the introduction of some constraints, it is possible to obtain high recognition rates up to 97.7%, and to transform the transcriptions of the formulas into T E X-syntax in order to achieve a convenient visualization of the results.
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