character recognition: Scope and challenges

04/07/23 Devnagari Character Recognition 1of 62

byVikas J. Dongre

Lecturer Electronics,Government Polytechnic Gondia


Contents

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


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).


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


Postal Address Recognition






Prime comitments


International Scenario (Source IBM)

Internet Users by Language

English

ChineseJapaneseSpanish

German

FrenchKoreanItalian

PortugueseDutch

Other


International Scenario (Source IBM)

Internet Users: Growth

EnglishChinese

Japanese

Spanish

German

FrenchKoreanItalian

Portuguese

Dutch

Other


Main Research Themes

Online character RecognitionPrinted Text RecognitionHandwriting RecognitionLanguage RecognitionGraphics Document RecognitionDocument UnderstandingTables and Forms ProcessingDocument Engineering


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.


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


Vowels and Corresponding Modifiers

Consonants

Half Form of Consonants with Vertical Bar


Examples of Combination of Half-Consonant and Consonant

Examples of Special Combination of Half-Consonant and Consonant.

Special Symbols


Character recognition Process

Image digit-zation using Scann

er

Image

pre-processin

g

Feature

extraction & Normalizati

on

Character

Classifier

Character

Segmentati

on

Storing

character in

text file


Image Preprocessing

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


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


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

HRa

m min

i

N

ii ppH log

1

• The character is then corrected by using:

ma

)tan( mayxx yy


Skew Correction


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


Global Transformation and Series Expansion Fourier Transforms Gabor Transform Wavelets Moments Karhunen-Loeve( KL) Expansion

Statistical Features

Zoning Crossings and Distances Projections


Geometrical and Topological Features

Extracting and Counting Topological Structures Measuring and Approximating the Geometrical

Properties Coding Graphs and Trees


Zoning


Structural Features




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


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…



Statistical Techniques Likelihood or Bayes classifier Clustering Analysis Hidden Markov Modeling (HMM) Fuzzy Set Reasoning Quadratic classifier



Neural Networks multilayer

perceptron (MLP) Kohonen's Self

Organizing Map (SOM)

Back Propagation algorithm

Support Vector Machine (SVM) algorithms



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


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.


Some Research results

Scanned document (input image)

04/07/23 Devnagari Character Recognition 37of 623704/07/23

Paragraph Segmentation


Segmented Paragraph

04/07/23 Devnagari Character Recognition 39of 623904/07/23

Segmented Paragraph


Zero pixel zone

Line Segmentation


Line Segmentation


Devnagari Word

Individual Devnagari symbols

Word Segmentation

Segmented word


Word Segmentation

Devnagari Word

Individual Devnagari symbols

Segmented word


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.


Character classification

Recognized characters

Input character

s

(54)

Correct=42Icorrect=9Not recognized: 3Accuracy=77.8 %

Features used:Filled AreaEuler NumberPerimeterConvex Area

Classifier used

Absolute difference


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.


Complexity in Indic writing


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.



•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.



•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.


Multilingual character recognition


Examples of multi-oriented documents


Two column documents with image


Image Document recognition



Video caption text recognition

Cargo container code recognition



Poster capturing License plate reading



Whiteboard reading Road sign recognition



Message on glass door with complex background

Document recognition on mobile phone


International journals related to Character recognition


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.


[email protected]

Mob: 9370668979


Acknowledgement

Friend, Philosopher and “ GUIDE”Dr. V.H. Mankar

for his consistent help and encouragement

Education

character recognition: Scope and challenges