Search space reduction for holistic ligature recognition in Urdu Nastaliq script (Bachelor thesis presentation)

Holistic Recognition of
Printed Arabic Script
Ligatures
Akram El-Korashy
Supervised by: Dr. Faisal Shafait
Deutsche Forschungszentrum für Künstliche Intelligenz (DFKI)
Kaiserslautern, Deutschland

Outline
● Introduction
○ Segmentation-free OCR for Arabic scripts
● Approaches Used
○ Features Extraction, and the Shape Context method
○ Machine Learning Techniques (Hierarchical
classification, Spectral Hashing, Random Forests)
● Improvements and Methodology
○ Shape Context weaknesses
○ New Features (dots, sizes, pixel-level matching)
○ Classification Methodology
● Experiments and Results
● Conclusion and Summary

Akram El-Korashy, Segmentation-free OCR, 14.08.12 1

Outline
● Introduction
● Approaches Used


Segmentation-free OCR for
Arabic scripts
ive

● Nastalique writing: Classify ligatures instead
of individual characters.
● Over 20,000 valid ligatures in the Urdu
language.
● Ease in the preprocessing, with difficulty in
feature extraction & classification.

Outline
● Introduction
● Approaches Used


Features Extraction, Shape
Context method
● Distribution of Points, Transformation
methods, Structural Analysis.

● Nabocr: Shape Context features vector.

● Contour Extraction.

● Shape Context is a shape descriptor
proposed by Belongie et al.

Features Extraction, Shape
Context method
● 4 histograms from 4 quadrants.

● Each histogram is
a sum of point histograms.

● Distance, Orientation

● Histogram: bins of ranges.


Outline
● Introduction
● Approaches Used


Hierarchical Classification
● Decomposing a classification problem into a
set of smaller problems.
● Useful with large numbers of categories.

● Efficiency of recognition.
● Can help improve accuracy

● Independent set of features for each branch.


Outline
● Introduction
● Approaches Used


Spectral Hashing
● Fast NN technique
● Feature vector into a binary code:
○ easily computed
○ small no. of bits
○ similarity mapping

● Calculating binary code:
○ maximum variance direction
(PCA)
○ sin eigenfn.


Outline
● Introduction
● Approaches Used


Random Forests
● Ensemble Classifier

Ensemble learning combines the predictions of different classifiers (decision trees) by collecting
independent votes from each tree and calculating the majority vote to give a prediction.

Outline
● Introduction
● Approaches Used


Shape context weaknesses
● Scale invariance

● Missing representation of dots

● Confusion between ligatures
that vary only in dots.


Outline
● Introduction
● Approaches Used


New Features
● Sizes of connected components

● Locations of connected components

○ above, below,
or interleaving

○ Grid location


New Features
● Pixel-level properties:

○ weights of regions
○ fill ratio

● Length, Width, Aspect Ratio

○ Invariance to scanning resolution
○ Setting reference size
○ Histogram of widths and heights

Outline
● Introduction
● Approaches Used


Classification Methodology
● Experiment set "1"
○ Spectral Hashing, reduction of number of
comparisons

● Experiment set "2"
○ Random Forests, hierarchy by recognizing the no. of
characters

● Experiment "3"
○ Random Forests, classification of alphabet symbols

● Spectral Hashing (sunvid project):

○ Training Dataset (~80,000 samples)

○ Test Dataset (~20,000 samples)

○ Different combinations of number of bits, number of
tables, tolerance bits (training different hash
structures in parallel)


● Random Forests (python milk):

○ Number of decision trees: 101
○ 70% of the attributes
○ 70% of the training samples

○ Reduced training dataset (~20,000 samples)
○ Test dataset of ~18,000 samples


● input

○ New features vector

Random Forest classifier
○ Classifying based on
no. of characters
1-character 2-character 3+ character
classifier classifier classifier

○ Classifying the Alphabet Symbols


Outline
● Introduction
● Approaches Used


Experiments and Results
● Spectral Hashing Results "1"
○ Effect of changing the number of tables
○ 7-bit-binary-code, 2 tolerance bits


Accuracy Best Reduction Hash (bits, tables,
tolerance)
81.5% 37538 (47.2%) 7, 9, 1
81% 31553 (39.7%) 7, 7, 1
80.5% 23975 (30.1%) 8, 9, 1
79.5% 20736 (26.1%) 7, 4, 1
78% 18737 (23.6%) 8, 7, 1
76% 15392 (19.4%) 7, 3, 1


● Significant reduction rates
○ Reduction down to 19% for a difference of 6% in
accuracy
○ Reduction down to 24% for a difference of 4% in
accuracy.

○ Reduction down to 47.2% for no accuracy loss.
○ Observation: Accuracy slightly higher than 1-NN for
reduction down to 57.6%


● Random Forest Results "2"

● Accuracy of 78.7% for 1, 2, 3, 4+ labels
● Accuracy of 45.4% for 1, 2, 3, 4, 5+ labels
● Accuracy of 20.7% for 1, 2, 3, 4, 5, 6+ labels
● Even worse with more partitioning


● Random Forest Results "2"
● Confusion matrix for 1, 2, 3+: alphabet
symbols can be separately classified.
test label / Recall
result
1 2 3+

1 1131 88 14 91.9%
2 16 94 531 17.2%
3+ 7 2 16627 99.9%
% true ___
positives
98% 51% 96.8%

● Alphabet symbols

○ 80.34 % for Random Forests "3"

○ Accuracy of 98.74 % for 1-NN classifier

○ 1-NN classifier can be used for recognition under
class 1.

○ Over 30% of ligatures are individual characters.


Outline
● Introduction
● Approaches Used


Conclusion and Summary
● Features vector can be improved.

● 1-NN improved efficiency by Spectral
Hashing: significant reduction

● Random Forests: can be used to separate
the 1-character alphabet symbols.

● Useful for overall performance improvement
on real text data.

Questions?
Future Work

Thank You

Search space reduction for holistic ligature recognition in Urdu Nastaliq script (Bachelor thesis presentation)

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