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Recognition of basic kannada characters in scene images using euclidean dis
- 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME
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RECOGNITION OF BASIC KANNADA CHARACTERS IN SCENE
IMAGES USING EUCLIDEAN DISTANCE CLASSIFIER
M.M.Kodabagi1
, Shridevi.B.Kembhavi2
1
Department of Computer Science and Engineering, Basaveshwar Engineering College,
Bagalkot-587102, Karnataka, India
2
Department of Computer Science and Engineering, Basaveshwar Engineering College,
Bagalkot-587102, Karnataka, India
ABSTRACT
Character recognition in scene images is a challenging visual recognition problem.
The research field of scene text recognition receives a growing attention due to the
proliferation of digital cameras and the great variety of potential applications, as well. Such
applications include robotic vision, image retrieval, intelligent navigation systems and
applications to provide assistance to visual impaired persons. In this paper, a novel
methodology for recognition of basic Kannada characters in scene images is proposed. It is
divided into two phases namely: training and testing. During training, zone wise horizontal
and vertical profile based features are extracted from training samples and knowledge base is
created. During testing, the test image is processed to obtain features and recognized using
euclidean distance classifier. The method has been evaluated on 460 Kannada character
images captured from 2 Mega Pixels cameras on mobile phones at various sizes 240x320,
600x800 and 900x1200 which contains samples of different sizes, styles and with different
degradations, and achieves an average recognition accuracy of 91%. The system is efficient
and insensitive to the variations in size and font, noise, blur, dark background, slant/tilt and
other degradations.
Keywords: Character Recognition, Scene Images, Zone Wise Horizontal and Vertical
Features, Euclidean Distance Classifier.
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING
& TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 2, March – April (2013), pp. 632-641
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2013): 6.1302 (Calculated by GISI)
www.jifactor.com
IJCET
© I A E M E
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1. INTRODUCTION
Character recognition in scene images is a challenging visual recognition problem.
Until a few decades ago, research in the field of Optical Character Recognition was limited to
document images acquired with flatbed desktop scanners. The usability of such systems is
limited as they are not portable because of large size of the scanners and the need of a
computing system. Moreover, the shot speed of a scanner is slower than that of a digital
camera. Hence research field of scene text recognition receives a growing attention due to the
proliferation of digital cameras and the great variety of potential applications, as well. Such
applications include robotic vision, image retrieval, intelligent navigation systems and
applications to provide assistance to visual impaired persons.
Recognition of characters from scene images is a very complex problem. Natural
scene images usually suffer from low resolution and low quality, perspective distortion,
complex background, font style and thickness, background as well as foreground texture,
camera position which can introduce geometric distortions, image resolution, shadows, non-
uniform illumination, low contrast and large signal dependent noise, slant and tilt as shown in
figure 1. The problem is significantly more difficult than recognizing text from scanned
images.
Figure 1. Sample Images of Display Boards
In this paper, a novel method for recognizing basic Kannada characters in natural
scene images is proposed. The proposed method uses zone wise horizontal and vertical
profile based features to extract features of character images. The method works in two
phases. During training phase, zone wise horizontal and vertical profile based features are
extracted from training samples and knowledge base is created. During testing, the test image
is processed to obtain features and recognized using euclidean distance classifier. The method
is evaluated on 460 Kannada character images captured from 2 Mega Pixels cameras on
mobile phones at various sizes 240x320, 600x800 and 900x1200 which contains samples of
different sizes, styles and with different degradations, and achieves an average recognition
accuracy of 91%. The system is efficient and insensitive to the variation in size and font,
noise, blur, dark background, slant/tilt and other degradations.
The rest of the paper is organized as follows; the detailed survey related to character
recognition of character in scene images is described in Section 2. The proposed method is
presented in Section 3. The experimental results and discussions are given in Section 4.
Section 5 concludes the work and lists future directions of the work.
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2. RELATED WORK
Some of the related works on character recognition of text in scene images are summarized
below:
A robust approach for recognition of text embedded in natural scenes is given in [11].
The proposed method extracts features from intensity of an image directly and utilizes a local
intensity normalization to effectively handle lighting variations. Then, Gabor transform is
employed to obtain local features and linear discriminant analysis (LDA) is used for selection
and classification of features. The proposed method has been applied to a Chinese sign
recognition task. This work is further extended integrating sign detection component with
recognition [12]. The extended method embeds multi-resolution and multi-scale edge
detection, adaptive searching, color analysis, and affine rectification in a hierarchical
framework for sign detection. The affine rectification recovers deformation of the text
regions caused by an inappropriate camera view angle and significantly improve text
detection rate and optical character recognition.
A framework that exploits both bottom-up and top-down cues for scene text
recognition at word level is presented in [13]. The method derives bottom-up cues from
individual character detections from the image. Then, a Conditional Random Field model is
built on these detections to jointly model the strength of the detections and the interactions
between them. It also imposes top-down cues obtained from a lexicon-based prior, i.e.
language statistics. The optimal word represented by the text image is obtained by
minimizing the energy function corresponding to the random field model. The method reports
significant improvements in accuracies on two challenging public datasets, namely Street
View Text and ICDAR 2003 compared to other methods. The test results showed that the
reported accuracy is only 73% and requires further improvement.
The hierarchical multilayered neural network recognition method described in [14]
extracts oriented edges, corners, and end points for color text characters in scene image. A
method called selective metric clustering which mainly deals with color is employed in [15].
A fast lexicon based and discriminative semi-Markov models for recognizing scene text are
presented in [16, 17]. An object categorization framework based on a bag-of-visual-words
representation for recognition of character in natural scene images is described in [18]. The
effectiveness of raw grayscale pixel intensities, shape context descriptors, and wavelet
features to recognize the characters is evaluated in [19]. A method for unconstrained
handwritten Kannada vowels recognition based upon invariant moments is described in [20].
The technique presented in [21] extracts stroke density, length, and number of strokes for
handwritten Kannada and English characters recognition. The method found in [22] uses
modified invariant moments for recognition of multi-font/size Kannada vowels and numerals
recognition. A model employed in [23] calculates features from connected components and
obtains 3k dimensional feature vectors for memory based recognition of camera-captured
characters. A character recognition method described in [24] uses local features for
recognition of multiple characters in a scene image.
After the thorough study of literature, it is noticed that, the some [18, 12, 23, 14] of the
reported methods work with limited datasets, other cited works [18, 17, 16] report low
recognition rates in the presence of noise and other degradations and very few works [18-22]
pertain to recognition of Kannada characters from scene images. Hence, more research is
desirable to obtain new set of discriminating features suitable for Kannada text in scene
images. In the current paper, zone wise horizontal and vertical profile based features are
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employed for recognition of Kannada characters in low resolution images. The detailed
description of the proposed methodology is given in the next section.
3. PROPOSED METHODOLOGY
The proposed method uses zone wise horizontal and vertical profile based features for
recognition of basic Kannada characters. The proposed method contains various phases such
as preprocessing, feature extraction, construction of knowledge base and character
recognition using euclidean distance classifier. The block diagram of the proposed model is
as shown in Figure 2. The detailed description of each phase is given in the following
subsections.
3.1 Preprocessing
The input character image is preprocessed for binarization, bounding box generation
and resized to a constant resolution of size 30×30 pixels. Further, the image is thinned.
Figure 2. Block Diagram of Proposed Model
3.2 Feature extraction
Features are extracted from the pre-processed image, each image is divided into 15
vertical zones and 15 horizontal zones, where size of each horizontal zone is 2*30 and the
size of each vertical zone is 30*2. Then sum of all pixels in every zone is determined as a
feature value. Finally we obtain 30 features that are stored in feature vector FV as described
in equations (1) to (5):
ܸܨ ൌ ሾሺܸ݁ݏ݁ݎݑݐܽ݁ܨ_݈ܽܿ݅ݐݎሻ ሺݏ݁ݎݑݐܽ݁ܨ_݈ܽݐ݊ݖ݅ݎܪሻሿ (1)
ܸ݁ݏ݁ݎݑݐܽ݁ܨ_݈ܽܿ݅ݐݎ ൌ ሾ ܸܨ ሿ 1≤ i ≤ 15 (2)
ݏ݁ݎݑݐܽ݁ܨ_݈ܽݐ݊ݖ݅ݎܪ ൌ ሾ ܨܪ ሿ 1≤ i ≤ 15 (3)
Where,
ܨܪ is a feature value of ith
horizontal zone and it is computed as shown in (4).
ܸܨ is a feature value of ith
vertical zone and it is computed as shown in (5).
Training
Sample
Images
Pre-Processing Feature
Extraction Database
Testing
Character
Image
Pre-Processing Feature
Extraction
Character
Recognition
Model
Recognised
character
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ܨܪ ൌ g ሺx, yሻ ሺ4ሻ
ଷ
ଵ
ଶ
ଵ
ܸܨ ൌ g ሺx, yሻ ሺ5ሻ
ଶ
ଵ
ଷ
ଵ
Where, g ሺx, yሻ is ith
zone that encompasses the chosen region of the character image. The
dataset of such feature vectors obtained from training samples is further used for construction
of knowledge base.
3.3 Construction of knowledge base
For the purpose of knowledge base construction, the images are captured from display
boards of Karnataka Government offices, names of streets, institute names, names of shops,
building names, company names, road signs, traffic direction and warning signs captured
from 2 Mega Pixels cameras on mobile phones. The images are captured at various sizes
240x320, 600x800, 900x1200 at a distance of 1 to 6 meters. All these images are used for
evaluating the performance of the proposed model. The images in the database are
characterized by variable font size and style, uneven thickness, minimal information context,
small skew, noise, perspective distortion and other degradations. The image database consists
of 460 Kannada basic character images of varying resolutions. Then from the database, 80%
of samples are used for training. During training, the features are extracted from all training
samples and knowledge base is organized as a dataset of feature vectors as depicted in (6).
The stored information in the knowledge base sufficiently characterizes all variations in the
input. Testing is carried out for all samples containing 80% trained and 20% untrained
samples.
ܤܭ ൌ ൣ ܸܨ୨ ൧ 1 ݆ ܰ ሺ6ሻ
Where, KB is knowledge base comprising feature vectors of training samples. FVj is a
feature vector of jth
image in the KB and N the number of training sample images as shown
in figure 3.
Figure 3. Sample Characters Images
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3.4 Training and Recognition using Euclidean Distance Classifier
After the data set is obtained and organized into knowledge base of basic Kannada
character images, training and recognition
classifier. The details of training and recognition are described in the following:
In this phase test image is processed to obtain zone wise horizontal and vertical profile based
features and stored into feature vector
determines minimum value between the test image and every record in the knowledge base
using the euclidean distance measure as in equation (7).
The minimum distance between the test image and the record in the knowledge base
is used to recognize the character.
font size, style and dark background
of all variations in font size, style and other degradations.
4. EXPERIMENTAL RESULT AND ANALYSIS
The proposed methodology has been evaluated for 460 basic Kannada character
images of varying font size and style, uneven thickness
degradations. The experimental results of processing a sample character image is described in
section 4.1. And the results of processing several other character images dealing with various
issues, the overall performance of the system are reported in section 4.2.
4.1 An Experimental Analysis for a Sample Kannada Character Image
The Character image with uneven thickness, uneven lighting conditions, and ot
degradations given in Figure. 4
generation, resized to a constant size of 30x30 pixels and thinned
and figure 4d.
a)
Figure 4. a) Test image
Further, the image is divided into 15 vertical zones and 15 horizontal zones
zone wise horizontal and vertical
organized into a feature vector FV
shown in Table 1.
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976
6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME
637
Training and Recognition using Euclidean Distance Classifier
After the data set is obtained and organized into knowledge base of basic Kannada
character images, training and recognition tasks are carried out using euclidean distance
The details of training and recognition are described in the following:
In this phase test image is processed to obtain zone wise horizontal and vertical profile based
ture vector FV1 using the above equation (1). Then classifier
determines minimum value between the test image and every record in the knowledge base
using the euclidean distance measure as in equation (7).
1≤ j ≤ N
between the test image and the record in the knowledge base
is used to recognize the character. The proposed methodology performs well for
background images. However, the method requires sufficient training
of all variations in font size, style and other degradations.
EXPERIMENTAL RESULT AND ANALYSIS
ogy has been evaluated for 460 basic Kannada character
g font size and style, uneven thickness, dark background
degradations. The experimental results of processing a sample character image is described in
section 4.1. And the results of processing several other character images dealing with various
issues, the overall performance of the system are reported in section 4.2.
An Experimental Analysis for a Sample Kannada Character Image
The Character image with uneven thickness, uneven lighting conditions, and ot
degradations given in Figure. 4a is initially preprocessed for binarization, bounding box
, resized to a constant size of 30x30 pixels and thinned as shown in figure
b) c) d)
Test image b) Image with Bounding Box c) Resized image
d) Thinned image
Further, the image is divided into 15 vertical zones and 15 horizontal zones
and vertical profile based features are computed for the images
FV as in (1) to (5). The experimental values of all zones
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
April (2013), © IAEME
After the data set is obtained and organized into knowledge base of basic Kannada
tasks are carried out using euclidean distance
In this phase test image is processed to obtain zone wise horizontal and vertical profile based
. Then classifier
determines minimum value between the test image and every record in the knowledge base
between the test image and the record in the knowledge base
The proposed methodology performs well for variability in
. However, the method requires sufficient training
ogy has been evaluated for 460 basic Kannada character
, dark background and other
degradations. The experimental results of processing a sample character image is described in
section 4.1. And the results of processing several other character images dealing with various
An Experimental Analysis for a Sample Kannada Character Image
The Character image with uneven thickness, uneven lighting conditions, and other
ation, bounding box
as shown in figure 4b, 4c
Resized image
Further, the image is divided into 15 vertical zones and 15 horizontal zones. Then, the
ures are computed for the images and are
experimental values of all zones are
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TABLE 1. Zone Wise Vertical and Horizontal Profile based Features of Sample Input
Image in Figure 4d
Feature Vector
FV
[Vertical_Features (3 12 6 6 8 10 8 4 10 8 4 4 5 14 0)
Horizontal_Features (0 7 9 8 8 8 11 10 12 4 4 4 6 11 0) ]
FV= [3 12 6 6 8 10 8 4 10 8 4 4 5 14 0 0 7 9 8 8 8 11 10 12 4 4 4 6 11 0]
The experimental values in Table 1 clearly depict the distribution of pixels in various
primitives of the character image. And these distributions are different from character to
character because of varying positions and shapes of primitives of basic Kannada characters.
This is demonstrated considering two sample images in Table 2.
TABLE 2. Vertical and Horizontal Features of Two Sample Images Demonstrating
Pixel Distribution Patterns
Character Image
Zone Wise Vertical and Horizontal Profile based
Features
21 5 5 8 8 16 4 4 20 4 9 14 4 5 9 0 25 8 8 7 9 8 8 8
11 5 4 6 7 22
15 7 8 9 10 11 8 8 10 9 8 7 6 9 18 3 23 3 3 5 13 15
13 16 3 4 7 11 14 10
The values in Table 2 clearly show that, the feature values in most of the
corresponding zones of the characters are distinct. The arrangement of these features into a
feature vector creates a pixel distribution pattern that makes samples distinguishable. It is also
observed that, the proposed zone wise features also take care of uncertainty in the appearance
of primitives of character image. After extracting features from test input image in Figure. 4a,
the euclidean distance classifier is used to recognize the character.
4.2 An Experimental Analysis Dealing with Various Issues
The proposed methodology has produced good results for scene images containing
basic Kannada characters of different size, font, and alignment with varying background. The
advantage lies in less computation involved in feature extraction and recognition phases of
the method. Since the feature set is reduced by taking sum of consecutive values of zone wise
horizontal and vertical profile based features. Hence, the proposed work is robust and
achieves an average recognition accuracy of 91%. The overall performance of the system
after conducting the experimentation on the dataset is reported in Table 3.
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TABLE 3. Overall System Performance
Character
Image
Number
of
Samples
Tested
Number of
Samples
Correctly
Recognized
Number
of
Samples
Miss
Classified
% of
Recognition
Accuracy
Character
Image
Number
of
Samples
Tested
Number of
Samples
Correctly
Recognized
Number
of
Samples
Miss
Classified
% of
Recognition
Accuracy
10 10 0 100 10 8 2 80
10 9 1 90 10 8 2 80
10 9 1 90 10 10 0 100
10 8 2 80 10 9 1 90
10 9 1 90 10 8 2 80
10 10 0 100 10 8 2 80
10 10 0 100 10 9 1 90
10 10 0 100 10 10 0 100
10 8 2 80 10 9 1 90
10 10 0 100 10 9 1 90
10 10 0 100 10 9 1 90
10 9 1 90 10 10 0 100
10 8 2 80 10 8 2 80
10 9 1 90 10 10 0 100
10 10 0 100 10 9 1 90
10 10 0 100 10 8 2 80
10 10 0 100 10 9 1 90
10 9 1 90 10 8 2 80
10 10 0 100 10 8 2 80
10 9 1 90 10 8 2 80
10 10 0 100 10 8 2 80
10 8 2 80
10 10 0 100
10 8 2 80
10 10 0 100
5. CONCLUSION
In this paper, a novel methodology for an approach to recognition of basic Kannada
characters from scene images is proposed. The proposed method uses zone wise horizontal
and vertical profile based features and euclidean distance classifier for basic Kannada
character recognition. The system works in two phases: training phase and testing phase.
Exhaustive experimentation was done to analyze horizontal and vertical profile based
features. The results obtained by considering zone wise horizontal and vertical profile
features and euclidean distance classifier are encouraging and it has been observed that the
system is robust and insensitive for several challenges like unusual fonts, variable lighting
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condition, noise, blur, orientation etc. The method is tested with 460 samples and gives
recognition accuracy of 91%. The proposed method can be extended for character recognition
considering new set of features and classification algorithms. This method can be extended to
recognize the characters of other languages.
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