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Text Recognition Techniques

Text Recognition Techniques. Group #2 Di Wu (d8wu) Ehren Choy (e3choy) Muhammad Qureshi (m2quresh) Mohammad Talha Khalid ( mtkhalid ). Problem. Recognize hand-written characters. Motivation. Hand-writing is a complex problem N eed AI techniques to help solve it. Remember A4?.

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Text Recognition Techniques

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  1. Text Recognition Techniques Group #2 Di Wu (d8wu) Ehren Choy (e3choy) Muhammad Qureshi(m2quresh) Mohammad TalhaKhalid (mtkhalid)

  2. Problem • Recognize hand-written characters

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

  4. Remember A4?

  5. Preprocessing • ColourConversion • Edge Detection • Canny Algorithm • Thinning & Skeletonization • Generate Predictor Variables

  6. Feature Extraction • Predictor Variables • Aspect Ratio • Junction and End Points • Loop • Ascenders and Descenders

  7. Application to Radial Basis • Input Layer • One neuron per predictor variable  • Hidden Layer • Calculate distance from center point • Output Layer • One output neuron for every category

  8. Fuzzy Systems • Pre-processing • Feature Extraction • “Structural recognition” • Extracting individual features • Fuzzy classification • Compare word structure with reference words

  9. Identifying structural features - English • Micro Vertical Line • Micro Horizontal Line • Micro Positive Slant • Micro Negative Slant

  10. Identifying structural features - Arabic Numerical decomposition of the word 4 sub-words 1 ascender 1 dot above 2 loops 2 descenders

  11. 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

  12. Fuzzy Network: Pros and Cons • Advantages • Lower computational requirements • Disadvantages • Not widely used in handwriting recognition problems

  13. 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

  14. 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

  15. Demo

  16. Neural Networks: Pros and Cons • Advantages • Automatic Learning • Quick Classification • Disadvantages • Efficiency • Locality

  17. Questions?

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