Computational Intelligence, SS08
2 VO 442.070 + 1 RU 708.070 last updated:
Course Notes (Skriptum)
Online Tutorials
Introduction to Matlab
Neural Network Toolbox
OCR with ANNs
Adaptive Filters
VC dimension
Gaussian Statistics
PCA, ICA, Blind Source Separation
Hidden Markov Models
Mixtures of Gaussians
Automatic Speech Recognition
Practical Course Slides
Animated Algorithms
Interactive Tests
Key Definitions
Literature and Links


Optical Character Recognition

It is often useful to have a machine perform pattern recognition. In particular, machines that can read symbols are very cost effective. A machine that reads banking checks can process many more checks than a human being in the same time. This kind of application saves time and money, and eliminates the requirement that a human perform such a repetitive task.

Problem Statement

A device is to be designed and trained to recognize the 26 letters of the alphabet. We assume that some imaging system digitizes each letter centered in the system's field of vision. The result is that each letter is represented as a 5 by 7 grid of real values.

The following figure shows the ``perfect'' pictures of all 26 letters.

Figure 1: The 26 letters of the alphabet with a resolution of 5 x 7.

However, the imaging system is not perfect and the letters may suffer from noise:

Figure 2: A ``perfect'' picture of the lettar ``A'' and 4 noisy versions (stabdard devistion of 0.2).

Perfect classification of ideal input vectors is required, and more important reasonably accurate classification of noisy vectors.