On-Line Handwritten Formula Recognition Using Statistical Methods

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Venue:
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Year:
1998

Citing 4
Cited 4

Improved On-Line Handwriting Recognition Using Context Dependent Hidden Markov Models

ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
An Investigation of the Use of Trigraphs for Large Vocabulary Cursive Handwriting Recognition

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
A Comparison Between Continuous and Discrete Density Hidden Markov Models for Cursive Handwriting Recognition

ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
HMM-based handwritten symbol recognition using on-line and off-line features

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Bibliography

ACM SIGGRAPH 2007 courses
A Practical Approach for Writer-Dependent Symbol Recognition Using a Writer-Independent Symbol Recognizer

IEEE Transactions on Pattern Analysis and Machine Intelligence
Progressive structural analysis for dynamic recognition of on-line handwritten mathematical expressions

Pattern Recognition Letters
Towards a web-based progressive handwriting recognition environment for mathematical problem solving

Expert Systems with Applications: An International Journal

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Abstract

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 TEX-syntax in order to achieve a convenient visualization of the results.