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Comparison of Handwritings Miroslava Božeková Thesis supervisor: Doc. RNDr. Milan Ftáčnik, CSc.
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Contents Goal Benefits Previous work Application Experiments Conclusion Future work Comparison of Handwritings2
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Goal One and more scanned images with handwritten text Same or different writer? Implement and make experiments Writer verification – TWO input images Image comes from: M. Bulacu and L. Schomaker, Text-Independent Writer Identification and Verification Using Textural and Allographic Features, 2007 Comparison of Handwritings3
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Benefits User-friendly program – no special knowledge expected – simple control – minimization of interaction Experiments for two input images – 100 samples from 40 writers – Comparison of 3 approaches – Best result 96,5 % accuracy by second approach Comparison of Handwritings4
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Two input images Comparison of Handwritings5
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Previous work 1 Srihari et al. – A large number of features – 2 categories: macrofeatures and microfeatures – Multilayer perceptron or parametric distributions – 96% accuracy Bensefia et al. – Extract the set of graphemes – Clustering graphemes for all documents in data set – Mutual information Comparison of Handwritings6
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Previous work 2 Schlapbach and Bunke – Hidden Markov Model based recognizers – Individual recognizer – Text lines as input – 2,5% error Bulacu and Schomaker – Probability distribution functions (PDFs) extracted from the handwriting images – Two levels – The texture level and the character-shape level. Comparison of Handwritings7
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Input data IAM Handwriting Database – forms of handwritten English text – for handwritten text recognizers, writer identification and verification experiments. – Scanning: resolution of 300dpi, saved as PNG images with 256 gray levels. – 1539 images from 657 authors – http://iamwww.unibe.ch/~fkiwww/iamDB/ http://iamwww.unibe.ch/~fkiwww/iamDB/ Comparison of Handwritings8
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Application Preprocessing Extraction of features Graphemes clustering – Modified hierarchical – Kohonen’s Self-Organizing Map (SOM) Three approaches: – First – feature vector – Second - feature vector + modified hierarchical clustering – Third – feature vector + SOM Comparison of Handwritings10
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Preprocessing Thresholding Line segmentation Slant correction Word segmentation Grapheme segmentation & normalization Comparison of Handwritings11
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Thresholding - Otsu 1979 Comparison of Handwritings12
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Line segmentation Comparison of Handwritings13 Arivazhagan, Srinivasan and Srihari 2007
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Slant detection and correction 1 What is slant? Comparison of Handwritings14
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Slant detection and correction 2 Comparison of Handwritings15
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Word segmentation 1 Comparison of Handwritings16 Image comes from: S. Srihari, Handwriting recognition, Automatic, 2006
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Word segmentation 2 Comparison of Handwritings17
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Grapheme segmentation What is grapheme? Image comes from: Marius Bulacu and Lambert Schomaker, A Comparison of Clustering Methods for Writer Identification and Verification, 2005 Comparison of Handwritings18
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Extraction of features Slant Density of handwriting Proportion Height of handwriting Distance between lines Block letters Comparison of Handwritings19
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Proportion, height and distance between lines height distance between lines ( Upp - Asc ) : ( Low - Upp) : ( Des - Low) Comparison of Handwritings20
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Block letters Comparison of Handwritings21
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First approach - feature vector Comparison of Handwritings22
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Self-Organizing Map Comparison of Handwritings23
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Modified hierarchical clustering 2 steps First step – 'closest' grapheme – using Euclidean distance – grapheme pairs (clusters). Second step – closest cluster (analogue with first step). Result – Percentage of numbers of clusters with graphemes coming from the same image. Comparison of Handwritings24
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Second and third approach Two thresholds for SOM Four thresholds for Modified hierarchical clustering Unique for each approach Comparison with thresholds – Similarity – Dissimilarity – Uncertain result, feature vector Comparison of Handwritings25
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Experiments ApproachwriterNOENOCRNOIRaccuracyaverage 1.Same Different 100 85 97 15 3 85% 97% 91% 2.Same Different 100 93 100 7070 93% 100% 96,5% 3.Same Different 100 86 98 14 2 86% 98% 92% Comparison of Handwritings26 NOE = number of experiments NOCR = number of correct results NOIR = number of incorrect results
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Three and more input images boolean verifyN (int n) { for (int k = 0; k < n; k++) { if (verify2(k, k+1) != true ) return false; } return true; } Comparison of Handwritings27
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One input image Extracted handwriting features Each feature is represented by a vector of numbers Comparison of Handwritings28
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Conclusion 3 approaches Experiments on 100 images from 40 different writers. The IAM Handwriting Database. 96,5 % accuracy. Comparison of Handwritings29
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Future work Preprocessing step – Deskew document – Noise reduction – Deskew lines – Rule lines Handwriting features Clustering methods More experiments with different threshold numbers Another handwriting database Comparison of Handwritings30
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The End Thank you for your attention June 12, 2008 Comparison of Handwritings31
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