Large Vocabulary Recognition of On-Line Handwritten Cursive Words
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical methods for speech recognition
Statistical methods for speech recognition
Improved On-Line Handwriting Recognition Using Context Dependent Hidden Markov Models
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
NPen/sup ++/: a writer independent, large vocabulary on-line cursive handwriting recognition system
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Speeding Up On-line Recognition of Handwritten Characters by Pruning the Prototype Set
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Password management using doodles
Proceedings of the 9th international conference on Multimodal interfaces
FreePad: a novel handwriting-based text input for pen and touch interfaces
Proceedings of the 13th international conference on Intelligent user interfaces
On-Line Handwriting Recognition System for Tamil Handwritten Characters
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Bi-modal handwritten text recognition (BiHTR) ICPR 2010 contest report
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Assamese online handwritten digit recognition system using hidden Markov models
Proceeding of the workshop on Document Analysis and Recognition
Improving on-line handwritten recognition in interactive machine translation
Pattern Recognition
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Pen-based interfaces aim at improving the manmachine interaction of many portable systems. While statistical models can be used to learn pen position sequences, they suffer from the huge variability exhibited by the speed of writing. To improve performance, invariance to the writing speed is needed. Trace segmentation is a technique that can be used to normalize the writing speed. This method is controlled by a parameter called resampling distance. A study of the resampling distance is presented here, along with an other approximation to the writing speed normalization called "derivatives normalization". The improvement using trace segmentation was 19.3% relative to the baseline, whilst the improvement using derivatives normalization was 47.3% relative.