1. ANN-based program for Tablet PC character recognition
Sebastian Tivis
For CS/ECE/ME 539: Intro to Artificial Neural Networks and Fuzzy Systems – Fall 2010

2. Motivation Must be extremely reliable to be natural.
1994 IBM study – users want ≥ 97% accuracy.
User adapting to software – not good.
Software should adapt to user’s handwriting style.
An ANN-based solution is therefore appropriate.

3. Problem Statement
Create a program in which the user can draw characters and the program tries to recognize the characters
Use various settings to improve network learning speed

4. Using artificial neural networks
Advantages
There is no information on the form of the function f(x) in advance.
There is a representative sample of inputs and outputs to be used as a training set
Disadvantages
Requires more processing power and memory
Can take a long time for noisy data

5. A Project overview Simplified version User draws character with mouse.
One char at a time
Alphanumeric
User draws character with mouse.
Program attempts to recognize it.
Program trained beforehand. A
Program
Input
Output

6. Program overview (2) C (or maybe MATLAB).
Back propagation algorithm used for training.
Three layers: input → hidden → output.
Bitmap images of characters used for training samples.

7. Training samples

8. Training the network Apply input (a bitmap) to the network
Calculate the output
Compare the resulting output with the desired output for the given input
Modify the weights for all neurons using the error
Repeat the process until the error reaches an acceptable value

9. Results Program is incomplete at this time. Want error of 3% or less.
Test on different people and with different characters.