useful in research for character recognition in general and Devnagari character recognition in perticular

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by
Vikas J. Dongre
Lecturer Electronics,
Government Polytechnic Gondia

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Contents
 Introduction
 Scope
 Features Of Devnagari Script
 Image Preprocessing
 Feature Extraction
 Character Classification
 Post processing
 Character Recognition challenges
 Current research results

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OCR (Optical Character Recognition)
 Character recognition is a part of pattern or object
recognition with special focus to Natural language
processing (NLP).
 “…a system that provides a full alphanumeric recognition of
printed or handwritten characters at electronic speed by
simply scanning the document.”
 Documents can be scanned through a scanner and then the
recognition engine of the OCR system interpret the images
and turn images of handwritten or printed characters into
ASCII data (machine-readable characters).

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Some applications
•Postal address reading
•Check reading
•Census data collection and processing
•Image document reading
•Digitizing old books in editable form
•Extended research:
• text to speech conversion (e-book reading)
•Visually impaired should be able to access
computers in their native language Indian
languages

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Postal Address
Recognition

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Prime
comitments

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International Scenario (Source IBM)
Internet Users by Language
English
Chinese
JapaneseSpanish
German
French
Korean
Italian
Portuguese
Dutch
Other

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International Scenario (Source IBM)
Internet Users: Growth
English
Chinese
Japanese
Spanish
German
French
KoreanItalian
Portuguese
Dutch
Other

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Main Research Themes
Online character Recognition
Printed Text Recognition
Handwriting Recognition
Language Recognition
Graphics Document Recognition
Document Understanding
Tables and Forms Processing
Document Engineering

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Introduction to
Devnagari character Recognition
 Devnagari Optical Character recognition (DOCR) is more
complicated as compared to English.
 various soft computing tools involved in other types of
pattern recognition and image processing can be used
for DOCR.

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Features Of Devnagari Script
 Devnagari is the most popular script in India.
 Hindi, the national language of India, is written in the
Devnagari script.
 It is also used for writing Marathi, Konkani, Sanskrit
and Nepali languages.
 Moreover, Hindi is the third most popular language in
the world.
 Alphabet set tends to be quite large.
 It has 11 vowels and 33 consonants as basic
characters.
 Compound characters can be formed by joining
characters in various ways.
 characters have a horizontal line at the upper part,
known as Shirorekha or headline

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Vowels and Corresponding Modifiers
Consonants
Half Form of Consonants with Vertical Bar

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Examples of Combination of Half-Consonant and
Consonant
Examples of Special Combination of Half-Consonant
and Consonant.
Special Symbols

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Character recognition Process
Image
digit-
zation
using
Scann
er
Imag
e
pre-
proce
ssing
Featur
e
extracti
on &
Normal
ization
Charac
ter
Classifi
er
Charac
ter
Segme
ntation
Storing
charac
ter in
text file

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Image Preprocessing
 Thresholding & Binarization
 Noise Reduction
 Segmentation
 Skew Detection And Correction
 Size Normalization
 Thinning

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Preprocessed Images (a) Original, (b) segmented (c)
Shirorekha removed (d) Thinned (e) image edging

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Slant Correction
• The dominant slope of the word is found from the slope corrected
words which gives the minimum entropy of a vertical projection
histogram. The vertical histogram projection is calculated for a range
of angles ± R. In our case R=60, seems to cover all writing styles. The
slope of the word, ,is found from:
ma
H
Ra
m
±∈
= minα i
N
i
i ppH log
1
∑=
−=
• The character is then corrected by using:
ma
)tan( mayxx −=′ yy =′

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Skew Correction

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Feature Extraction
 A set of features are extracted for each class that helps
distinguish it from other classes, while remaining
invariant to characteristic differences within the class
 Various methods are:
 Global Transformation and Series Expansion
 Statistical Features
 Geometrical and Topological Features

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Global Transformation and Series Expansion
 Fourier Transforms
 Gabor Transform
 Wavelets
 Moments
 Karhunen-Loeve( KL) Expansion
Statistical Features
 Zoning
 Crossings and Distances
 Projections

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Geometrical and Topological Features
 Extracting and Counting Topological
Structures
 Measuring and Approximating the
Geometrical Properties
 Coding
 Graphs and Trees

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Zoning

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Structural Features

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Character Classification
 Template Matching.
 Statistical Techniques.
 Neural Networks.
 Support Vector Machine (SVM) algorithms.
 Combination classifier.
OCR systems extensively use the methodologies of
pattern recognition, which assigns an unknown sample
to a predefined class. Various methods are

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Template Matching
 Euclidean Distance
 Mahalanobis, Jaccard or Yule similarity measures
 K-Nearest Neighbor measurements
This is the simplest way of character recognition. The
recognition rate of this method is very sensitive to noise
and image deformation. Various methods are
Character Classification…

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Character Classification…
 Statistical Techniques
 Likelihood or Bayes classifier
 Clustering Analysis
 Hidden Markov Modeling (HMM)
 Fuzzy Set Reasoning
 Quadratic classifier

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Character Classification…
 Neural Networks
 multilayer
perceptron (MLP)
 Kohonen's Self
Organizing Map
(SOM)
 Back Propagation
algorithm
 Support Vector
Machine (SVM)
algorithms

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Character Classification…
Combination Classifier
 ANN and HMM
 K-Means and SVM
 MLP and SVM
 MLP and minimum edit
 SVM and ANN
 fuzzy neural network
 NN, fuzzy logic and genetic algorithm

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Post processing
 save in text file
 Refine OCR output using spell check ,
grammar check and other knowledge
source comparisons
 other applications using standard word
processors.

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Some Research results
Scanned document (input image)

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Paragraph Segmentation

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Segmented Paragraph

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Segmented Paragraph

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Zero pixel zone
Line Segmentation

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Line Segmentation

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Devnagari Word
Individual Devnagari
symbols
Word Segmentation
Segmented word

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Word Segmentation
Devnagari Word
Individual Devnagari
symbols
Segmented word

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Some observations
 Experiments with degraded text images show that
the chief source of error is at the level of
segmentation of characters.
 A similar situation exists for recognition of hand
written texts.
 Error rates are at acceptable levels for the other
stages i.e. line segmentation, word segmentation,
character recognition etc.

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Character classification
Recognized
characters
Input
characters
(54)
Correct=42
Icorrect=9
Not recognized:
3
Accuracy=77.8 %
Features used:
Filled Area
Euler Number
Perimeter
Convex Area
Classifier
used
Absolute
difference

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Research Publications
Vikas J Dongre, Vijay H Mankar, “A Review of Research on
Devnagari Character Recognition”, International Journal of
Computer Applications (0975 – 8887) Volume 12– No.2, pp.
8-15, November 2010.

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Complexity in Indic writing

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Devnagari Character recognition challenges -1
•Devnagari is Two dimensional script as consonants are
modified in many ways to form a meaningful letter.
•Same is also true for its recognition.
•The recognizer has to identify all the modifiers present in a
letter.
•Generated ISCII codes or Unicode are the combined
properly to display the digitized document.

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Devnagari Character recognition challenges -2
•Compound letter segmentation.
•Upper and lower modifier segmentation.
•Left and right modifier segmentation
•Separating anuswara (.) and full stop from noise.
•Understanding punctuation marks in the document.
•Unconnected compound letters handwritten document.
•Connected simple letters in handwritten document.

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Devnagari Character recognition challenges -3
•India is multilingual country. More than one language is
used in a document frequently.
•Recognition of more than one language at a time is a
great challenge.
•Initially Language recognition is to be done by looking
into the properties of the script.
•English–Hindi language discrimination is moderately
simple as compared to Marathi-Hindi.
•Various forms in Banks uses three languages (Marathi-
State language, Hindi-National language and English-
International language). This this work is still more
challenging.

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Multilingual character recognition

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Examples of multi-oriented documents

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Two column documents with image

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Image Document recognition

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Image Document recognition
Video caption
text recognition
Cargo container
code recognition

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Image Document recognition
Poster capturing License plate reading

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Image Document recognition
Whiteboard reading Road sign recognition

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Image Document recognition
Message on glass door with
complex background
Document recognition on
mobile phone

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International journals related to
Character recognition

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Conclusion
Development in character recognition will boost word
processing and image understanding.
Devnagari character recognition will help readers to
listen to Indian literature using computers and PDA or e-
book readers.
It will help in language translation which is complex
problem in multilingual country like India where each
state have its own language.
Many modern innovative applications will evolve which
is the need of time in this information age.
This will help in information processing to a large
extent.

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dongrevj@yahoo.co.in
Mob: 9370668979

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Acknowledgement
 Friend, Philosopher and “ GUIDE”
Dr. V.H. Mankar
for his consistent help
and encouragement