The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
HMM Based On-Line Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical methods for speech recognition
Statistical methods for speech recognition
Developing HMM-Based Recognizers with ESMERALDA
TSD '99 Proceedings of the Second International Workshop on Text, Speech and Dialogue
Signal Representations for Hidden Markov Model based on-line Handwriting Recognition
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Online Handwriting Data Acquisition Using a Video Camera
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Visual Input for Pen-Based Computers
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Visual input for pen-based computers
Visual input for pen-based computers
On-line cursive handwriting recognition using hidden Markov models and statistical grammars
HLT '94 Proceedings of the workshop on Human Language Technology
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One of the most promising methods of interacting with small portable computing devices such as personal digital assistants is the use of handwriting. However, for data acquisition touch sensitive pads, which are limited in size, and special pens are required. In order to render this communication method more natural Munich & Perona [11] proposed to visually observe the writing process on ordinary paper and to automatically recover the pen trajectory from video image sequences. On the basis of this work we developed a complete handwriting recognition system based on visual input. In this paper we will describe the methods employed for pen tracking, feature extraction, and statistical handwriting recognition. The di erences compared to classical on-line recognition systems and the modi cations in the visual tracking process will be discussed. In order to demonstrate the feasibility of the proposed approach evaluation results on a small writer independent unconstrained handwriting recognition task will be presented.