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Character Recognition Using Machine Learning Techniques

Character Recognition Using Machine Learning Techniques. Matthew Peterson Muktesh Khole Aseem Gogte Jeremy Kindseth. Problem Statement and Assumptions. “ Box” letters for analysis Identify alphanumeric symbols in documents or images (A-Z, a-z, 0-9). Image Character. A B C.

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Character Recognition Using Machine Learning Techniques

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  1. Character Recognition Using Machine Learning Techniques Matthew Peterson MukteshKhole AseemGogte Jeremy Kindseth

  2. Problem Statement and Assumptions “Box” letters for analysis Identify alphanumeric symbols in documents or images (A-Z, a-z, 0-9) ImageCharacter A B C

  3. Problem Difficulty Harder Easier r s M k H a u e d a t e H Not easily separable; surrounding artifacts; no consistent font D R O F S N A R T M E Skewed but separable Easy to box Simple to Decompose Complex Fonts, boxed Simple Fonts, boxed

  4. Hypotheses • Accuracy: K-Means Clustering + EM Algorithm can be used for accurate classification of alphanumeric symbols • K-Means Clustering is as accurate as pixel-by-pixel detection using MSE • Efficiency: K-Means Clustering is faster than pixel-by-pixel detection using MSE

  5. Features • Analysis of geometric locations of centroids X axis Y axis

  6. Training and Testing Criteria

  7. Accuracy Results Training: OCRA – Testing: Arial Training: OCRA – Testing: Handwritten Training: Frankenset– Testing: Handwritten Training: SuperSet– Testing: Handwritten Training: Frankenset– Testing: OCRA Training: SuperSet– Testing: OCRA Training: SuperSet– Testing: Arial Training: OCRA – Testing: OCRA Training: Frankenset– Testing: Arial

  8. Time Performance • SuperSet Training vs. Pix-by-Pix • Testing against Fonts/Handwritten • K = 11 used

  9. Hard Problems • Trouble pairs • 2;7 • 3;6;8;9 • O;Q;0 • V;U • B;D;R • I;l (uppercase “i”, lowercase “L”) • Scaling problems (X vs. x; C vs. c)

  10. Conclusions Method • Random starting locations did not work • Scaling is important, especially for upper/lowercase differentiation (scaled to 128 x 128) • “Adaptable K” K-Means Clustering may be interesting • SuperSet method could be used to handle transformed data Results • Pixel-by-Pixel is more accurate when SuperSetused for training • For K-Means Clustering, SuperSetworked better for training • SuperSet increases memory usage and processor time – O(n!) • Frankenset is not a training model

  11. Conclusions Regarding Hypotheses • Accuracy: K-Means Clustering + EM Algorithm can be used for accurate classification of alphanumeric symbols • K-Means Clustering is as accurate as pixel-by-pixel detection using MSE • Efficiency: K-Means Clustering is faster than pixel-by-pixel detection using MSE All Hypotheses Affirmed K-Means Clustering Effectively Serves as Method for Compressing Alphanumeric Data

  12. References Sheshadri, Karthik, PavanAmbekar, Deeksha Prasad, Ramakanth Kumar. “An OCR System for Printed Kannada Using K-means Clustering.” Industrial Technology (ICIT) 2010 IEEE International Conference on (March 2010): 14-17.

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