this power point presentation provides a brief introduction to image processing and pattern recognition and its related research papers including conclusion

1. Image Processing and Pattern Recognition
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2. Contents
• Introduction to Image Processing
• Introduction to Pattern Recognition
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3. Introduction to Image Processing
• Image processing is the field that deals with the type of
signal for which the input is an image and output is also
an image.
• As it’s name suggest, it deals with the processing on
image.
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4. Introduction to Image processing
Image Processing
Analog Image Processing Digital Image Processing
• 2D Analog signals
• Television image
• Develops a digital system
that performs operation on
an digital image
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5. Introduction to Image Processing
Applications:
• Image sharpening and restoration
• Medical field
• Remote sensing
• Transmission and encoding
• Machine/robot vision
• Color processing
• Pattern recognition
• Video processing
• Microscopic imaging
• Others
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6. Introduction to Pattern Recognition
• Pattern recognition is the scientific discipline whose goal
is “The classification of objects into a number of
categories or classes”.
• It is an integral part of most machine intelligence
system, build for decision making.
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7. Introduction to Pattern Recognition
Application:
• Character (number/letter) recognition
• Computer aided diagnosis
• Speech recognition
• Data mining and knowledge discovery
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8. Paper1 (Elsevier Journal)
Title :
A ranking-based feature selection approach for handwritten
character recognition
Year : 2018
Authors:
Nicole Dalia Ciliaa, Claudio De Stefanoa,**, Francesco
Fontanellaa, Alessandra Scotto di Frecaa
(Dipartimento di Ingegneria Elettrica e dell’Informazione, University of Cassino and
Southern Lazio, Via Di Biasio 43, 03043 Cassino (FR), ITALY)
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9. • The aim of feature selection is that of reducing the
computational cost of the classification task, in an
attempt to increase, or not to reduce, the classification
performance.
• In the framework of handwriting recognition, the large
variability of the handwriting of different writers makes
the selection of appropriate feature sets even more
complex and have been widely investigated.
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10. • To overcome some of the drawbacks by adopting a feature-
ranking-based technique: we considered different univariate
measures to produce a feature ranking and we proposed a
greedy search approach for choosing the feature subset able
to maximize the classification results.
• In the experiments, we considered one of the most effective
and widely used set of features in handwriting recognition to
verify whether our approach allows us to obtain good
classification results by selecting a reduced set of features.
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11. • Related work:
The feature selection step consists of three basic steps:
A search procedure for searching candidate feature
subset.
A feature subset evaluation strategy
A stopping criterion
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12. • The feature set considered:
The feature of SET1 measures three properties of a
segmented image representing an input sample related to
the concavity, to the contour and to the character surface.
Total 132 features.
The feature set SET2 is used for describing the MFEAT
(multiple feature) dataset, publicly available from the UCI
machine learning repository. Total 649 features divided into
6 groups.
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13. FOU:76 Fourier coefficients of the character shapes
ZER:47 Zernike moments
MOR:6 morphological features
KAR:64 karhunen-love coefficients
PIX: 240 pixel averages in 2X3 window
FAC: 216 profile correlation
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14. • Feature evaluation:
 Univariate measure
-Chi-square(CS)
-Relief(RF)
-Information gain(IG)
-Gain ratio(GR)
-Symmetrical uncertainty(SU)
 Subset measures
-Consistency criterion(CC)
-Correlation based feature selection criterion(CFS)
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15. • Experimental Results:
Three sets of experiments are performed:
 First, we use SET1 for representing the sample of three real word
database, namely NIST, Rimes database, and a db of characters
segmented from postal address(PD)
 Second, we perform a similar analysis but using the SET2.
 Third, we characterized the group features exhibiting higher
discriminant power for both the SET1 and SET2.
 K-NN, Bagging and Random forest are used for evaluation of
selected feature subset.
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16. fig: Experimental results on NIST database using K-NN (a), Bagging (b)
and Random Forest (c) classifiers 18

17. • Conclusion:
The result of experiments suggests that the idea of using
a reduced feature set, namely that obtained by
discarding the features in lower position of the ranking,
can provide very interesting result.
Reducing the computational complexity of the whole
recognition system with very limited effect on the
classification performance.
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18. • Future work:
To use other more complex and computationally
expensive feature selection technique on reduced feature
subset, obtained by selecting the features in highest
position on the ranking.
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19. Paper 2(IEEE journal)
Title:
Multilingual Character Segmentation and Recognition
Schemes for Indian Document Images
Year : 2018
Author:
Parul Sahare and Sanjay B. Dhok
(Department of Electronics & Communication, Centre of VLSI & Nanotechnology,
Visvesvaraya National Institute of Technology, Nagpur)
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20. • robust algorithms for character segmentation and recognition are
presented.
• These documents generally suffer from their layout organizations,
local skews and low print quality and contain intermixed texts
(machineprinted and handwritten).
• In proposed character segmentation algorithm, primary
segmentation paths are obtained using structural property of
characters, whereas overlapped and joined characters are
separated using graph distance theory.
• Finally, segmentation results are validated using highly accurate
Support Vector Machine (SVM) classifier.
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21. • For proposed character recognition algorithm, three new
geometrical shape based features are computed. First and
second features are formed with respect to the center pixel
of character, whereas neighborhood information of text
pixels is used for the calculation of third feature.
• For recognizing the input character, k-Nearest Neighbor (k-
NN) classifier is used, as it has intrinsically zero training time.
• Comprehensive experiments are carried out on different
databases containing printed as well as handwritten texts.
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22. Fig: structure of proposed work for character segmentation and recognition
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25. • Conclusion:
 In this paper, two new algorithms are proposed for character
segmentation and recognition(FCDF & FCCF) for multilingual Indic
documents consisting of printed and handwritten texts.
 Highest SR of 98.86% is obtained on proprietary database of Latin script.
 Proposed recognition algorithm shows highest accuracy of 99.84% on
Chars74k numerals database.
 Comparatively 0.2-0.5% higher RRs are obtained when k-NN is used with
city block distance relative to other distances. Proposed algorithm is 2.7-
12.4% more efficient on numerals databases as compared to databases
contain alphabets.
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