Title: The New Text and Graphical Input Device: Compact Biometrical Data Acquisition Pen
1The New Text and Graphical Input Device Compact
Biometrical Data Acquisition Pen
- Václav Matoušek, Ondrej Rohlík,
- Pavel Mautner, Jürgen Kempf
Department of Computer Science and
Engineering University of West Bohemia in Pilsen
2Outline
- Data acquisition device The BiSP pen
- Handwritten text recognition
- Hidden Markov models
- Experimental results
- Future Work
3Input Devices Overview
- off-line (static)
- scanners
- cameras
- on-line (dynamic)
- electronic pens
- digitizers, tablets
- cameras
- mouse
4Input Device The BiSP Pen
- Electronic pen is used for data acquisition
built at the University of Applied Sciences in
Regensburg, Germany
5Input Device Writing
6Input Device Signals
7Handwritten Text Recognition
- Objective To convert handwritten sentences or
phrases in analog form (off-line or on-line
sources) into digital form (ASCII or Unicode). - isolated character recognition (TM, DTW, NN)
- word recognition (HMMs)
- gesture recognition
8Handwritten Text
hand printed characters spaced discrete
characters cursive script words
9Input Device Signals
10Signal Description
- Pairs of x and y signals are transformed
into sequence of primitives
Primitive (observation) Signal trend Signal trend
Primitive (observation) x y
1 ? ?
2 ? ?
3 ? ?
4 ? ?
11Hidden Markov Models
- left-to-right model
- (used mostly in speech recognition)
12Hidden Markov Models
- Training Baum-Welch algorithm
- Recognition Backward algorithm
- Matrices that describes the word model (A, B, ?)
are decomposed after the training one model for
each letter is obtained
13Word HMM Decomposition
14Word HMM Composition
15Experimental Results
- method have been tested on three independent data
sets of various sizes - limited number of letters used in our data sets
15 - reduced complexity of tagging the training set
Vocabulary size 1649 2198 5129
Recognition rate () 88 90 82
Recognition time (min) 17-26 27-49 360
16Future Work
- to speed up the algorithm to achieve
real-time recognition - incorporation of language models to improve the
recognition rate - special attention will be paid to signature
analysis and signature verification
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