Model Structure Selection and Training Algorithms for an HMM Gesture Recognition System
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Magic Wand: A Hand-Drawn Gesture Input Device in 3-D Space with Inertial Sensors
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Using mobile phones to write in air
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Closing the loop between intentions and actions
Adjunct proceedings of the 25th annual ACM symposium on User interface software and technology
A survey on smartphone-based systems for opportunistic user context recognition
ACM Computing Surveys (CSUR)
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This paper presents an online handwritten character recognition system. The whole system includes three parts: acceleration signal detection, signal processing and recognition by Hidden Markov Model (HMM). In hardware aspect, a mini-board with a three-dimensional accelerometer and a microcontroller is used to get real time acceleration values and send them to a terminal continuously. After effective section extraction and lowpass filtering, different quantizing methods based on acceleration orientation are used to quantize numerous data into small integral vectors. At last, we use HMM to do the recognition. For the experiments with 10 Arabic numerals, this system shows a high Recognition Rate (R.R.) of 94.29% in the database of 42 models for every Arabic numeral. This system could be used to reduce the size of handheld devices by discarding number keys and make human computer interaction more convenient and interesting.