Text Recognition Techniques

Group #2Di Wu (d8wu)

Ehren Choy (e3choy)Muhammad Qureshi (m2quresh)

Mohammad Talha Khalid (mtkhalid)

Problem

• Recognize hand-written characters

Motivation• Hand-writing is a complex problem• Need AI techniques to help solve it

Remember A4?

Preprocessing

• Colour Conversion

• Edge DetectionoCanny Algorithm

• Thinning & SkeletonizationoGenerate Predictor

Variables

Feature Extraction

• Predictor Variables oAspect Ratio

o Junction and End Points

oLoop

oAscenders and Descenders

Application to Radial Basis

• Input LayeroOne neuron per predictor variable 

• Hidden LayeroCalculate distance from center point

• Output LayeroOne output neuron for every category

Fuzzy Systems

• Pre-processing• Feature Extraction

– “Structural recognition”– Extracting individual features

• Fuzzy classification– Compare word structure with

reference words

Identifying structural features - English

• Micro Vertical Line• Micro Horizontal Line• Micro Positive Slant• Micro Negative Slant

Identifying structural features - Arabic

• 4 sub-words• 1 ascender• 1 dot above• 2 loops• 2 descenders

Numerical decomposition of the word

Compare word structure with reference words

• Fuzzy classifier classifies word’s membership in different classes

• Uses Fuzzy K nearest neighbor algorithm– Calculate distance between word & training samples

Fuzzy Network: Pros and Cons

• Advantages– Lower computational requirements

• Disadvantages– Not widely used in handwriting recognition

problems

Genetic Programming

• Generating graph from input image – Break down image to line segments

• Compute fitness using fitness function – Edge Deviation – Graph Deviation

• Crossover operation – Replace a path between two vertices in one graph

with a path between two matching vertices in another graph

Pros and Cons

• Advantages – Easier to generate a large solution set by creating

hybrids from a smaller initial set • Disadvantages

– Long running time due to high number of possible combinations of graph pairs for computing fitness

Demo

Neural Networks: Pros and Cons

• Advantages– Automatic Learning– Quick Classification

• Disadvantages– Efficiency– Locality

Questions?