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Problem Tool Harder problem Experiments Result Optical Character Recognition using Bayesian Networks Ioannis Klasinas iklasinas@telecom.tuc.gr July 11, 2007 Ioannis Klasinas iklasinas@telecom.tuc.gr Optical Character Recognition using

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Problem Tool Harder problem Experiments Result

Optical Character Recognition using Bayesian Networks

Ioannis Klasinas iklasinas@telecom.tuc.gr July 11, 2007

Ioannis Klasinas iklasinas@telecom.tuc.gr Optical Character Recognition using Bayesian Networks

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Problem Tool Harder problem Experiments Result

Problem

Letter Recognition Using Holland-Style Adptive Classifiers, Peter

W. Frey, David J. Slate

English capital letters 20000 instances (bitmap fonts) 45x45 pixel bitmap Images distorted (linear magnification, aspect radio, horizontal/vertical wrap) 16 features extracted 82.7% accuracy Others 93,6% (Statlog ALLOC80)

Ioannis Klasinas iklasinas@telecom.tuc.gr Optical Character Recognition using Bayesian Networks

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Problem Tool Harder problem Experiments Result

Weka

Weka (http://www.cs.waikato.ac.nz/ml/weka/) Various classification methods Used Bayes networks 87.5% accuracy, 4 parents per node

Ioannis Klasinas iklasinas@telecom.tuc.gr Optical Character Recognition using Bayesian Networks

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Problem Tool Harder problem Experiments Result

Digit OCR

Scanned handwritten digits 16x16 grayscale bitmaps 9200 instances Threshold to convert to b/w Extracted features Normalized as above NRR-1:94.5%, Bayes:38.2%

Ioannis Klasinas iklasinas@telecom.tuc.gr Optical Character Recognition using Bayesian Networks

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Problem Tool Harder problem Experiments Result

Experiments

Experimented with

1 threshold 2 max parents number

Best result for threshold=0.2, max parents=16

Ioannis Klasinas iklasinas@telecom.tuc.gr Optical Character Recognition using Bayesian Networks

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Problem Tool Harder problem Experiments Result

Threshold

Figure: Bitmaps, for threshold -0.5/0/0.5

Ioannis Klasinas iklasinas@telecom.tuc.gr Optical Character Recognition using Bayesian Networks

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74 75 76 77 78 79 80 81 82 83 84 2 4 6 8 10 12 14 16 18 th=-0.5 th=-0.4 th=-0.3 th=-0.2 th=-0.1 th=0 th=0.1 th=0.2 th=0.3 th=0.4

Figure: Results

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Problem Tool Harder problem Experiments Result

Discussion

Handwritten OCR tough problem Weka unpredictable Bayesian networks inferior to other approaches for this problem More appropriate features needed

Ioannis Klasinas iklasinas@telecom.tuc.gr Optical Character Recognition using Bayesian Networks