Investigation on Algorithm for Handwritten Gujarati OCR

Ph.D. Synopsis

Submitted To

Gujarat Technological University

For the Degree

of

Doctor of Philosophy

in

Electronics and Communication Engineering

By

Mikita R. Gandhi

Enrollment No: 149997111007

Supervisor:

Dr. Vishvjit Thakar

Associate Professor and Head

Information and Communication Technology,

Sankalchand Patel University,

Visnagar.

Co-Supervisor:

Dr. Hetal N. Patel

Professor & Head

Electronics and Communication Department,

A.D.Patel Institute of Technology,

New V. V. Nagar.

Table of Contents

1. Title of the Thesis and Abstract............................................................................. 1

2. Brief description on the state of the art of the research topic................................ 2

3. Objective and Scope of the work........................................................................... 4

4. Original contribution by the thesis......................................................................... 4

5. Methodology of Research, Results / Comparisons................................................ 4

6. Achievements with respect to objectives............................................................... 17

7. Conclusions............................................................................................................ 18

8. List of publication arising from the thesis............................................................. 19

9. References.............................................................................................................. 20

1

1. Title of the Thesis and Abstract

1.1 Title of the Thesis:

Investigation on Algorithm for Handwritten Gujarati OCR

1.2 Abstract:

Optical Character Recognition is getting much more attention because by this the computer

learns and recognizes the regional languages pretty well and if it successes, then it opens a

whole new world of endless possibility.The machine printed characters are accurately

recognizable which has solved many problems and hence commercialized in routine use but

the recognition of hand written characters are very difficult and methods of recognition of

hand written documents is still a subject of active research. There is no common algorithm is

possible for all Indian language, because each Indian language has its own features and

restrictions. In Gujarat state, Gujarati is the commercial language and most of the

communication in Government office, schools and private sectors is done in Gujarati.

Handwritten Gujarati OCR system was developed for handwritten amount on cheque,

automatic reading of marks from answer sheet and a learning application for education

system. The research work is mainly focused on implementation of robust algorithm for

Handwritten Gujarati OCR.

The KNN and SVM classifiers were used on different feature extraction methods like pixel

count ratio, object gradient; geometry, profile, local binary pattern, ceter-symmetric local

binary pattern and wavelet transform methods. Furthermore hybrid feature extraction

methods were used for increase the performance of character recognition. The other novel

approach of automated features extracted was implemented using Deep learning. The

extracted features were given to SVM for handwritten character classification. For increasing

recognition rate of characters, pretrained Deep Neural network (Alexnet) has been used and

implemented three different application: Handwritten Guajarati Numeral to speech

conversion, character to speech conversion and Automatic Handwritten Marks Recognition.

KNN, SVM and Deep Neural Networks gives recognition accuracy of 98.14%,98.72% and

99.30% for Numeral, 92.37%, 92.21% and 97.65% for characters and 92.64%, 92.93% and

97.73% for combining Numerals and characters respectively.

2

2. Brief descriptions on the state of the art of the research topic

As the world move closer to the concept of the “paperless office,” more and more

communication and storage of documents is performed digitally. Documents and files that

were once stored physically on paper are now being converted into electronic form in order to

facilitate quicker additions, searches, and modifications and also doing this, life of such

documents are prolonged. The advances in character recognition were limited to the

extraction of English language character for both digital and handwritten. The character

recognition of Indian languages can help authors, novelist, and many people to recognize the

Indian characters and even to extract old heritage documents. The research work is

approximately negligible for handwritten character recognition in general for Indian

languages and Gujarati language in particular. In Gujarat State, all Government agency

documents are written in Gujarati language. The software is available for printed Gujarati

OCR but recognition of handwritten character is still changing exertion.

Basic block diagram of OCR system is shown in figure 2.1. There are five major stages are

like preprocessing, segmentation, representation, training and recognition and post

processing.

Figure 2.1 Basic block diagram

Preprocessing is required to make the raw data usable in the descriptive stages of character

analysis like smoothing, sharpening, binarize the image, remove background and extracting

the required information. Segmentation converts the document into separate character by first

segment the lines, then line segments the words and from words to individual characters

which is used by classifier. In representation stage, the set of features are extracted to

distinguished one class of the images from other class. KNN, SVM, Neural Network, Deep

Learning like classifier are used for training and recognition.

The Gujarati OCR worked was initiated by Sameer Antani et.al.[1] on printed Gujarati

Script.KNN and hamming distance classifier was applied on 15 characters; 30 samples for

3

each character and got 67 % and 41.33% accuracy respectively. Using template matching and

wavelet transform coefficients [2], Shah S. K et.al. attained 72.30 % accuracy for printed

Gujarati OCR, Ankit K. Sharma et.al. [3] worked on zoning method and using multilayer

feed forward neural network classifier achieved 95.92% accuracy for handwritten Gujarati

Numerals and Archna vyas et.al.[4] got 96.99% accuracy using KNN. Using hybrid feature

space method and SVM classifier A. Desai [5] has recognize forty handwritten Gujarati

characters with 86.66% accuracy. The Zonal Boundary was successfully detected [6] by

Jignesh Dholakia et.al. using zoning method. Swital J. Macwan et al [7] has applied discrete

wavelet transform method on Gujarati Handwritten and got 89.46% accuracy. V. A. Naik et

al have used different structural and statistical features for recognition of handwritten

numerals and acquired 95% accuracy [8] and Dinesh Satange et al. obtained 90% accuracy

using Multi Layer Perception[9].

Ashutosh Aggarwal et.al. [10]has worked on gradient based feature extraction and SVM

classifier for Hindi handwritten character recognition. LBP features are used for Bangla digits

recognition in 2015 [11] and achieved 96.7% accuracy using KNN classifier; the same LBP

feature applied on Persian/Arabic handwritten digit recognition[12]. Sekhar Mandal et al [13]

proposed algorithm for machine-printed character recognition in Bangla language using two

dimensional wavelet transform and gradient information. Saleem Pasha et al have solved

problem of handwritten recognition for Kannada language using statistical featuresand

wavelet transform [14].

Two stage CNN network was used by Shibaprasad Sen et al [15] for online Bengali

handwritten character recognition and gain 99.40% accuracy. Akm Ashiquzzaman et al

worked on 10 different layer of CNN architecture for Arabic handwritten digit and achieved

97.4% accuracy [16]. Chaouki Boufenar et al shown the three different approach of Deep

learning methods for handwritten Arabic character recognition: i) scratch approach; (ii)

transfer learning approach and (iii) fine-tuning approach [17].

Table 2.1 shows the Gujarati Numerals and Characters used for research work.

Numerals

૦ ૧ ૨ ૩ ૪ ૫ ૬ ૭ ૮ ૯

Characters

ક ખ ગ ઘ ચ છ જ ઝ ટ ઠ ડ ઢ ણ ત થ દ ધ ન પ ફ

બ ભ મ ય ર લ વ શ ષ સ હ ળ ક્ષ જ્ઞ શ્ર અ ઋ ઈ ઉ ઊ

Table 2.1 Gujarati Numerals and Characters

4

3. Objective and Scope of work

3.1 Objective:

To develop an algorithm for handwritten Gujarati OCR features to recognize

numerals and Character.

To design an Optical Character Recognition system for handwritten Gujarati

Numerals.

To design an Optical Character Recognition system for handwritten Gujarati

Characters.

To design an Optical Character Recognition system for combined handwritten

Gujarati Numerals and Characters.

3.2 Scope of work:

The research work is useful to automatic detection of amount written in Gujarati on

bank cheque, marks written on answer sheet and in Gujarati numeral and characters

learning application.

Handwritten Gujarati numeral and characters to speech conversation

The implemented algorithms can be useful for recognition of Gujarati text modifier.

4. Original contribution by the thesis

Develop different feature extraction methods along with creation of database for

Gujarati Handwritten numerals and characters.

Three different classification methods: KNN, SVM and Deep learning was used for

recognition

Hybrid features with the above listed three classification methods.

Transfer learning approach of Deep learning is used for better accuracy.

Three applications are developed:

Gujarati handwritten Numeral to speech conversion

Gujarati handwritten Character to speech conversion

Automatic Handwritten Marks recognition.

5. Methodology of research, results and comparisons

There is no standard database available for handwritten Gujarati OCR, the database was

created for research work which containing 5000 samples for Numerals and 10,000 samples

for characters from different age people.

5

5.1 Feature Extraction Method:

Method 1: Pixel Count Ratio

The image is resized into 64 X64 matrix. Some morphological operations are applied on it.

Then image was divided into 8X 8 zone and total 64 zones are created. From each zone, the

ratio of number of white pixels/number of Black pixels is taken as features so total 64

features are generated.

Figure 5.1 shows the image of Guajarati numeral ‘1’ and its 64 different zones.

5.1 pixel count ratio

Method 2: Object Gradient

The gradient magnitude and gradient direction is used as features. First the image is divided

into 9 sub images. The code is assigned for 30° span of direction. So total 12 code assigned to

each sub image. The total 12X9 =108 features are obtained from single image .

Figure 5.2 shows the image of Guajarati numeral ‘0’ and its 9 different zones. For each sub

image gradient magnitude and gradient direction is computed and further, each white pixels

gradient direction is observed, then checks that specific pixel lies in which span, according to

span the code is assign to that pixel.

Figure 5.2 Object Gradient

6

Method 3: Object Geometry

In this method [18] object Geometry is used as features, geometry features like horizontal

line, vertical line, right diagonal and left diagonal lines, area and Euler number. For these

feature extraction image is divided into 9 sub images. From each sub images, first starting

point and intersection points are founded, and then numbers of line segments are counted in

particular direction like horizontal, vertical, right diagonal and left diagonal lines. The first

four features of each sub images are values of these lines, computed by equation (1).

Value =1 - ((number of lines /10) * 2) …………… (1)

Next four features are computed using length of lines. If particular line is not available, than

consider value =-1, else normalize the length consider as feature value. Last feature is

considered as area of sub image. So total 9 features from each sub image X 9 sub images =81

features and Euler no of image is consider as one another features. Hence total 82 features are

computed.

Figure 5.3 Object Geometry

Figure 5.3 shows the one of the sub image of digit ‘0’. The 9 features are calculated as below:

No. of segments: 3

o No. of horizontal lines : 0

o No. of vertical lines : 0

o No. of right diagonal lines :2

o No. of left diagonal lines :1

Value is calculated by:

Value =1 - ((number of lines /10) * 2)

So the first four features are:

o Value of horizontal lines : 1

o Value of vertical lines :1

o Value of right diagonal lines:0.6

7

o Value of left diagonal lines :0.8

Next 4 features are calculated as:

Length= (total no. of pixels in a particular direction) / (total no. of all pixels belonging to

skeleton)

Total no. of all pixels belonging to skeleton: 20

If there is no pixels in particular line than consider length = -1

o Normalized Length of all horizontal lines :-1

o Normalized Length of all vertical lines :-1

o Normalized Length of all right diagonal lines :12/20 =0.60

o Normalized Length of all left diagonal lines :5/20 =0.25

The 9th feature from each zone is computed as:

o Normalized Area of the Skeleton= (Total no. of all pixels belonging to

skeleton) /(size of sub image)

o Normalized Area of the Skeleton = 20/289 = 0.0692

Method 4: Character Profile

Objects horizontal, vertical, right diagonal and left diagonal profile are considered as

features. The image is resized into 50 X 50. So total 298 features =50 horizontal + 50

vertical + 99 right diagonal+ 99 left diagonal profile are calculated.

Figure 5.4 show the character profile for numeral ‘1’.

Figure 5.4 Character Profile

8

Method 5: Local Binary Pattern

Local Binary Patterns (LBP) is mostly used as feature extraction method in recognition of

Face, fingerprint, texture. It operates on image pixels and replace its value with decimal

number. In image, each central pixel value is compared with its eight neighboring pixels, if

the neighboring pixel has less value than assign 0 else assign 1 to that pixel. Considering top

left corner as a first bit and rotate clock wise manner generates eight bit binary code. The

central pixel value is replaced by the decimal value of that binary code. The histogram of

these decimal values is used as features.

Figure 5.5 shows LBP code generation. The generated Binary code is 11000010, that is

equivalent to 194 in decimal. To implement the rotation invariant features and reduce the size

of feature vector, Uniform LBB is used.

Figure 5.5 (a) Input image (b) The LBP (8,1) operator (c) LBP coded block

A local binary pattern is called uniform if its uniformity measure is at most 2.For example,

the patterns 00000000 (0 transitions), 01110000 (2 transitions) and 11001111 (2 transitions)

are uniform and the patterns 11001001 (4 transitions) and 01010011 (6 transitions) are not

uniform. In uniform LBP mapping there is a separate output label for each uniform pattern

and all the non-uniform patterns are assigned to a single label. So, there are 58 uniform

patterns and 1 non uniform pattern, total 59 point feature vector is considered.

For obtain Uniform LBP features in this research work, binary image is first converted into

gray image and then image is further divided into various size of blocks, suppose size is

12X12, so total 16 blocks are generated and from each block 59 features are obtained, so total

features are 16 X 59 + 59 features from whole image =1003 features are obtained.

Method 6: Center Symmetric Local Binary Pattern.

Center Symmetric Local Binary Pattern (CSLBP) is extension of LBP, in which difference of

opposite pixel values are taken, if the difference is greater than some threshold value the

assign bit 1 else assign bit 0; so the length of histogram of CSLBP is 16 point.

9

Figure 5.6 shows the generation of CSLBP code. consider the threshold value is 8 and the

difference between opposite pixel values 80-70= 10 and 10 is greater than 8 so put 1, same

way the value of other opposite are counted and binary code is generated. Here, the binary

code is 1011 and its equivalent decimal code is 11.

Figure 5.6 Computation of CSLBP (8,1) with threshold= 8

Like LBP, CSLBP features are obtain by converting image into gray scale, and the divide

into blocks. Total number of features for 12X12 block size is 16 blocks X 16 features + 16

features of whole image= 272 .

Method 7: Wavelet Transform

Wavelet transform is usually used for representing and analyzing image. Image is represented

by the two dimensional matrix; the wavelet transform is applied first row wise and then

column wise, so final image is divided into four sub bands: [LL, HL, LH, HH], each sub band

gives image’s approximation detail, horizontal detail, vertical detail and diagonal detail.

Figure 5.7 shows the first level wavelet transform. The approximation details are used as

features in the research work. The number of features is 256, 64 and 16 for level 2, 3 and 4

respectively.

Figure 5.7 2-D Wavelet Transform

10

5.2 Classification Method

Classification is a task which assign object one of the classes from predetermined classes.

Here five fold cross validation is used for classification.

5.2.1 K-Nearest Neighbors Classifier

In pattern recognition, KNN is a method for classifying objects based on the closest

training examples in the feature space.

KNN is a type of lazy learning where the function is only approximated locally and

all computation is deferred until classification.

The simplest of all machine learning algorithms: an object is classified by a majority

vote of its neighbors, with the object being assigned to the class most common

amongst its k nearest neighbors. k is a positive integer, typically small.

If k = 1, then the object is simply assigned to the class of its nearest neighbor.

K-NN assumes that the data is in feature space.

The data can be scalars. Since the points are in feature space, they have notion of

distance.

Given an m-by-n data matrix X, which is treated as m (1-by-n) row vectors x1, x2,

..., xm, the various distances between the vector xs and xt are defined as follows:

o Euclidean distance

o City block metric

o Cosine distance

o Correlation distance

( )( )1

( )( ) ( )( )

ss t tst

s ts s s t t t

x x x xd

x x x x x x x x

1 s tsts s t t

x xd

x x x x

1

n

st sj tj

j

d x x

( )( )st s t s td x x x x

11

Table 5.1 shows the accuracy of numerals, characters and combining numerals and characters

for different feature extraction method using KNN classifier.

Feature Extraction

Methods

Accuracy

Numerals Characters Mix

Pixel Count Intensity 97.18% 78.12% 80.65

Gradient based 98.14% 89.06% 89.81

Object Geometry based 90.02% 67.25% 71.38

Object Profile 95.82% 76.71% 79.19

Local Binary Pattern 97.92% 88.12% 88.97

Center Symmetric Local

Binary Patterns

97.92% 87.65% 89.38

Wavelet transform 97.86% 81.40% 84.02

Table 5.1 KNN Classifier Accuracy for Different Feature Extraction Method

Figure 5.8 shows the accuracy of hybrid feature extraction method. By concatenating CSLBP

and Gradient features, character recognition accuracy reach up to 92.37%.

Figure 5.8 hybrid feature extraction method for characters

Figure 5.9 shows the accuracy of hybrid feature extraction method for mixed numerals and

characters. By concatenating CSLBP and Gradient features, the recognition accuracy reach

up to 92.64%.

87

88

89

90

91

92

93

Local Binary Pattern + Gradient

Center Symmetric Local Binary Patterns

+ Gradient

Wavelet Transform + Gradient

% A

ccu

racy

Features Extraction Method

Hybrid Feature Extraction Methods

12

Figure 5.9 hybrid feature extraction method for mixed numerals and characters

5.2.2 Support Vector Machine

When there is no idea about data, support vector machine (SVM) extremely work

well.

SVM’s are very excellent when we have no idea on the data.

It works with unconstructed and semi constructed information data like images, text

and trees.

The kernel strategy is main power of SVM . With a specific kernel functionality , it is

possible to deal with any kind of complex problem

In contrast to neural networks, SVM is not made up for local optima.

It scales extremely good to high dimensional data.

SVM is always gives better result than ANN

SVM also required the good kernel selection and large dataset.

SVM takes long training time than other classifier.

Common kernels

o Linear K(x,z) = xTz

o Quadratic K(x,z) = (1+xTz)2

o Polynomial K(x,z) = (1+xTz)d

o RBF K(x,z) = exp-(||x-z||2)

88

89

90

91

92

93

Local Binary Pattern + Gradient

Center Symmetric Local Binary Patterns

+ Gradient

Wavelet Transform + Gradient

% A

ccu

racy

Features Extraction Method

Hybrid Feature Extraction Methods

13

Table 5.2 shows the accuracy of numerals, characters and combining numerals and characters

for different feature extraction method using SVM classifier.

Feature Extraction

Methods

Accuracy

Numerals Characters Mix

Pixel Count Intensity 96.90% 76.28 % 81.05%

Gradient based 98.72% 89.57% 92.10%

Object Geometry based 90.70% 67.59% 70.56%

Object Profile 93.50% 64.45% 70.08%

Local Binary Pattern 97.50% 85.99% 88.26%

Center Symmetric Local

Binary Patterns 95.82% 83.3% 86.21%

Wavelet transform 97.40% 84.93% 86.68%

Table 5.2 SVM Classifier Accuracy for Different Feature Extraction Method

Figure 5.10 shows the accuracy of hybrid feature extraction method. By concatenating

wavelet and Gradient features, character recognition accuracy reach up to 92.37%.

Figure 5.10 hybrid feature extraction method for characters

Figure 5.11 shows the accuracy of hybrid feature extraction method for mixed numerals and

characters. By concatenating wavelet and Gradient features, the recognition accuracy reach

up to 92.64%.

85

86

87

88

89

90

91

92

93

Local Binary Pattern + Gradient

Center Symmetric Local Binary Patterns +

Gradient

Wavelet Transform + Gradient

% A

ccu

racy

Features Extraction Method

Hybrid Feature Extraction Methods

14

Figure 5.11 hybrid feature extraction method for mixed numerals and characters

5..3 Deep Learning:

In deep learning, a computer model learns to perform classification tasks directly

from images, text, or sound.

Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding

human-level performance.

Models are trained by using a large set of labeled data and neural network

architectures that contain many layers.

Deep learning requires substantial computing power. High-performance GPUs have a

parallel architecture that is efficient for deep learning.

Most deep learning methods use neural network architectures, which is why deep

learning models are often referred to as deep neural networks.

5.3.1 Convolutional Neural Network (CNN)

Most popular algorithms for deep learning with images composed of an input layer,

an output layer, and many hidden layers in between.

CNN usually consists of

o Convolution Layer :

In convolutional layer used to extract the features from image, different filters

with different weight are applied on the input layer of the image, so the

outcomes of this, two dimensional feature maps are generated.

88.5 89

89.5 90

90.5 91

91.5 92

92.5 93

Local Binary Pattern + Gradient

Center Symmetric Local Binary Patterns +

Gradient

Wavelet Transform + Gradient

% A

ccu

racy

Features Extraction Method

Hybrid Feature Extraction Methods

15

o Pooling Layer or Sub Sampling :

Poling layer operate on each feature map. It can decrease the spatial dimension

but cannot decrease the depth of the feature map. It trims down the amount of

parameter and computation in network.

o Fully Connected Layer (Classification) :

Fully connected network connect features obtained by previous layers into its

number of classes.

Figure 5.12 Architecture of CNN

Figure 5.13 Implementation of CNN

16

Figure 5.12 shows the CNN architecture, which has two convolutional layers, two pooling

layers and one fully connected layers. It also consist two ReLU layers, which is increase the

non- linearity of image by replacing all negative values by zero. Also the Softmax layer is

used for highlights the largest value and suppresses the value which is significantly below the

maximum value.

Implementation of CNN network is shown in figure 5.12. The center blocks of the image

shows sequence of layers, left side shows the size of feature map of each layers and right side

shows number of feature maps with its size. The output of each layer is shown in figure 5.13.

Table 5.3 shows the accuracy for numerals and characters for input size 64X64 and 96X96

respectively.

CNN Layers No. of

Filters

% Accuracy for

Numerals

% Accuracy for

Characters

Input size:

64X64

Input size:

96X96

Input size:

64X64

Input size:

96X96

Convolutional layer 1 20 80.7 94.4 70.15 74.15

Convolutional layer 2 40

Convolutional layer 1 40 88.25 95.1 72 76.25

Convolutional layer 2 80

Table 5.3 Proposed CNN Architecture Accuracy for Numerals and Characters

5.3.2 Pretrained Network Approach

Pretrained network is a previously trained network on a large standard datasets like similar

problems that we want to solve. It already knows how to extract features which are

informative and more powerful. More than million images are given to train this type

Network and its output also classified into approximately 1000 class.

Figure 5.14 Pretrained Network Approach

17

Pretrained networks like alexnet, vgg16, vgg19, googlenet, resnet18, resnet50, sufflenet are

used for new task with only feature extraction purpose or transfer learning approach, as

shown in figure 5.14. Alexnet is used as pretrained Network. Alexnet returns a pretrained

AlexNet model and it contains 25 layers. The ImageNet database is used to train this model

and its classified the image into 1000 classes such as different animals, mouse, pencils, cup,

ambulance.

A. Pretrained network as feature extractor

In this approach, Alexnet model is used for feature extraction and these extracted features are

given to SVM classifier for training and tasting purpose. Features are extracted using 20

layers of Alexnet that is layer ‘fc7’. The recognition accuracy for numerals, characters and

mix database is shown in table 5.4

B. Transfer Learning Approach

In this approach, all the layers of the pretrained Alexnet has been used expect last three

layers. The new task has been carried out by replacing those last three layers with fully

connected layers, Softmax layers and classification output layers. The recognition accuracy

for numerals, characters and mix database for this approach is shown in table 5.4

Database SVM classification Approach

Transfer Learning Approach

Numerals 97.40% 99.30%

Characters 86.50% 97.65%

Mixed 89.33% 97.73%

Table 5.4 Pretrained Network Approach

6. Achievements with respect to objectives

Gujarati Handwritten numerals and characters database has been created in which

5000 samples for numerals and 10,000 samples for characters.

Handwritten Gujarati numerals, total 10 class, are recognized with 98.14% accuracy

using gradient feature extraction method and KNN classifier, 98.72% accuracy using

gradient features and SVM classifier and achieve 99.30% accuracy using Transfer

Learning approach in Deep Learning.

Handwritten Gujarati characters, total 40 class, are recognized with 92.37% accuracy

using CSLBP + gradient based hybrid feature extraction method and KNN classifier,

92.21% accuracy using wavelet + gradient based hybrid features and SVM classifier

and achieve 97.65% accuracy using Transfer Learning approach in Deep Learning.

18

Handwritten Gujarati numerals and characters, total 48 class, are recognized with

92.64% accuracy using CSLBP + gradient based hybrid feature extraction method and

KNN classifier, 92.93% accuracy using wavelet + gradient based hybrid features and

SVM classifier and achieve 97.73% accuracy using Transfer Learning approach in

Deep Learning.

Three applications are implemented:

1) Handwritten Guajarati Numerals to speech conversion.

2) Handwritten Guajarati Characters to speech conversion.

3) Automatic Handwritten Marks Recognition.

7. Conclusion:

The hand written Gujarati numeral recognition algorithm was successfully developed

using large number (5000) of test images with accuracy of 99.30%.

The hand written Gujarati character recognition algorithm was successfully developed

using large number (10,000) of test images with accuracy of 97.65%.

The hand written Gujarati number and character recognition algorithm was

successfully developed using large number (15,000) of test images with accuracy of

97.73 %.

19

8. List of publications

1) Mikita Gandhi, V.K.Thakar, H.N.Patel, “Handwritten Gujarati Numeral Recognition

using wavelet Transform”, Journal of Applied Science and Computation (JASC),

Volume VI, Issue IV,2019

2) Mikita Gandhi, V.K.Thakar, H.N.Patel, “Gujarati Handwritten Character Recognition

Using Convolutional Neural Network”, Journal of Emerging Technologies and

Innovative Research (JETIR) , Volume VI, Issue V, May 2019

20

References

1) Antani S, Agnihotri L “Gujarati character recognition”, In: Proceedings of fifth

international conference on document analysis and recognition, 1999 (ICDAR’99), pp

418–421

2) Shah SK, Sharma A, “Design and implementation of optical character recognition

system to recognize Gujarati script using template matching”, J. Inst Eng (India)

Electron Telecommunication Eng.,2006.

3) Ankit K. Sharma, Dipak M. Adhyaru, Tanish H. Zaveri, Priyank B Thakkar,

“Comparative analysis of zoning based methods for Gujarati handwritten numeral

recognition”, 5th Nirma University International Conference on Engineering

(NUiCONE),IEEE 2015

4) Vyas, A. N. ,Goswami, M. M., “Classification of hand written Gujarati numerals”,

IEEE transactions on pattern analysis and machine intelligence, pp.1231- 1237,2015

5) A. Desai, “Support vector machine for identification of handwritten Gujarati

alphabets using hybrid feature space”, CSIT, springer, January, 2015.

6) Dholakia J, Negi A, Rama Mohan S, “Zone identification in the printed Gujarati text”,

In: Proceedings of the eight international conference on document analysis and

recognition, 2005 (ICDAR’05).

7) Swital J. Macwan, Archana N. Vyas, "Classification of Offline Gujarati Handwritten

Characters", International Conference on Advances in Computing, Communications

and Informatics (ICACCI), 2015

8) Dr. Dinesh Satange, Dr. P E Ajmire, Fozia I. Khandwani, “Offline Handwritten

Gujrati Numeral Recognition Using MLP Classifier”, International Journal of Novel

Research and Development, Volume 3, Issue 8 August 2018

9) Sekhar Mandal, Sanjib Sur ,Avishek Dan “Handwritten Bangla Character Recognition

in Machine-printed Forms using Gradient Information and Haar Wavelet”, 2011

International Conference on Image Information Processing.

10) Ashutosh Aggarwal, Rajneesh Rani, RenuDhir, "Handwritten Devanagari Character

Recognition Using Gradient Features", International Journal of Advanced Research in

Computer Science and Software Engineering (ISSN: 2277-128X), Vol. 2, Issue 5, pp.

85- 90, May 2012

11) T. Hassan, H. Khan, “Handwritten BangIa Numeral Recognition using Local Binary

Pattern”, 2nd Int'l Conf. on Electrical Engineering and Information & Communication

Technology (lCEEICT),2015.

21

12) M. Pietikäinen, A. Hadid, G. Zhao, T. Ahonen (2011), ‘Local Binary Patterns for Still

Images, Computer Vision Using Local Binary Patterns’, Chapter 2, Computational

Imaging and Vision 40, Springer-Verlag London Limited, pp 13 – 47.

13) Sekhar Mandal, Sanjib Sur ,Avishek Dan “Handwritten Bangla Character Recognition

in Machine-printed Forms using Gradient Information and Haar Wavelet”, 2011

International Conference on Image Information Processing.

14) Saleem Pasha, M.C.Padma, “Handwritten Kannada Character Recognition using

Wavelet Transform and Structural Features” International Conference on Emerging

Research in Electronics, Computer Science and Technology – 2015

15) Sen S., Shaoo D., Paul S., Sarkar R., Roy K. ,“Online Handwritten Bangla Character

Recognition Using CNN: A Deep Learning Approach”, In: Bhateja V., Coello Coello

C., Satapathy S., Pattnaik P. (eds) Intelligent Engineering Informatics. Advances in

Intelligent Systems and Computing, vol 695. Springer, Singapore,2018.

16) Alom, M.Z, Sidike, P., Taha, T.M., Asari, V.K.., “Handwritten Bangla Digit

Recognition Using Deep Learning” Journal Neural Processing Letters; 45, pp: 703-

725,2017.

17) C. Boufenar, A. Kerboua, M. Batouche, "Investigation on deep learning for off-line

handwritten Arabic character recognition", Cogn. Syst. Res., 2017.

18) M. Blumenstein, B. K. Verma and H. Basli, A Novel Feature Extraction Technique

for the Recognition of Segmented Handwritten Characters, 7th International

Conference on Document Analysis and Recognition (ICDAR ’03) Eddinburgh,

Scotland: pp.137-141, 2003.

19) Patel CN, Desai AA , “Segmentation of text lines into words for Gujarati handwritten

text”, In: Proceedings of international conference on signal and image processing,

2010 (ICSIP’10), IEEEXplore,15–17.

20) Dapping Tao, Xu Lin, Lianwen Jin, “Principal Component 2-D Long Short-Term

Memory for Font Recognition on Single Chinese Characters”, IEEE

TRANSACTIONS ON CYBERNETICS, VOL. 46, NO. 3, MARCH 2016

21) Devendra K Sahu and C. V. J awahar, “Unsupervised Feature Learning for Optical

Character Recognition”, In: Proceedings of the 13th International Conference on

Document Analysis and Recognition,2015 (ICDAR’15)

22) Mohamed Dahi, Noura A. Semary, and Mohiy M. Hadhoud, “Primitive Printed

Arabic Optical Character Recognition using Statistical Features”, IEEE Seventh

22

International Conference on Intelligent Computing and Information Systems, 2015

(ICICIS'15).

23) Tanzila Saba, “Language Independent Rule Based Classification of Printed &

Handwritten Text”, IEEE International Conference on Evolving and Adaptive

Intelligent Systems (EAIS),December ,2015.

24) Abdeljalil Gattal, “Segmentation-Verification Based on Fuzzy Integral for Connected

Handwritten Digit Recognition”, IEEE transaction on Image Processing Theory,

Tools and Applications, 2015.

25) Patel CN, Desai AA (2013) Gujarati handwritten character recognition using hybrid

method based on binary tree-classifier and k-nearest neighbour. Int J Eng Res

Technology, II(6):2337–2345.

26) D. Bradley and G. Roth, “ Adaptive thresholding using the integral image”, Journal of

Graphics tools, Vol.12, No.2,pp.13-21, Jun 2007.

27) Wojciech Bieniecki, Szymon Grabowski and Wojciech Rozenberg “Image

Preprocessing for Improving OCR Accuracy” International Conference on

Perspective Technologies and Methods in MEMS Design, MEMSTECH 2007 Pp.75-

80, 23-26 May 2007.

28) Luis R. Blando’, Junichi Kanai, and Thomas A. Nartker “Prediction of OCR

Accuracy Using Simple Image Features” IEEE Proceedings of the Third International

Conference on Document Analysis and Recognition, Vol.1 PP. 319 – 322, 14-16 Aug

1995

29) Chinmay Chinara, Nishant Nath, Subhajeet Mishra, “ A Novel Approach to Skew-

Detection and Correction of English Alphabets for OCR” IEEE Student Conference

on Research and Development (SCOReD), pp.5-6 241 – 244, Dec. 2012

30) Xiaoling Fu, Yazhuo Xu, Lijing Tong “Document Image Skew Adjusting Based on

the Feedback Information Recognized By OCR” IEEE 3rd International Conference

on Communication Software and Networks (ICCSN), pp. 376 – 378, 27-29 May

2011.

31) E.Kavallieratou, N.Fakotakis and G.Kokkinakis “Handwritten Character Recognition

based on Structural Characteristics” IEEE 16th International Conference on Pattern

Recognition, 2002. Proceedings. Vol.3 pp.139 - 142 .

32) Hanchuan Peng, , Fuhui Long, Zheru Chi ” Document Image Recognition Based on

Template Matching of Component Block Projections ” IEEE Transactions on Pattern

Analysis and Machine Intelligence, Vol. 25, No. 9, pp. 1188 - 1192 September 2003.

23

33) PEPE SIY, C. S. CHEN “Fuzzy Logic for Handwritten Numeral Character

Recognition” IEEE Transactions on Systems, Man and Cybernetics,

Vol.4,No.6, pp.570-575

34) Salvador Espan˜a-Boquera, Maria Jose Castro-Bleda, Jorge Gorbe-Moya, and

Francisco Zamora-Martinez “Improving Offline Handwritten Text Recognition with

Hybrid HMM/ANN Models” IEEE Transactions on Pattern Analysis and Machine

Intelligence, Vol. 33, No.4,pp.767-779,APRIL,2011.

35) D. Bradley and G. Roth, “ Adaptive thresholding using the integral image”, Journal of

Graphics tools, Vol.12, No.2,pp.13-21, Jun 2007.

36) Koga, M. “Camera-based Kanji OCR for mobile-phones: practical issues” Eighth

IEEE International Conference on Document Analysis and Recognition, Vol. 2, pp.

635 –639, 29 Aug.-1 Sept. 2005

37) Lund, W.B. “Error Correction with In-domain Training across Multiple OCR System

Outputs” IEEE International Conference on Document Analysis and Recognition

(ICDAR),pp. 658 – 662, 18-21 Sept. 2011.

38) Bhattacharya, U. “Handwritten Numeral Databases of Indian Scripts and Multistage

Recognition of Mixed Numerals”, IEEE Transactions on Pattern Analysis and

Machine Intelligence, Vol.31, No.3 , pp. 444 - 457, March 2009

39) Kavallieratou, E. “New algorithms for skewing correction and slant removal on word-

level [OCR]” The 6th IEEE International Conference on Electronics, Circuits and

Systems,Vol.2, pp. 1159 – 1162, 5-8 Sep 1999

40) Nikhil Pai, Vijaykumar S. Kolkure,”Optical Character Recognition: An

Encompassing Review”, International Journal of Research in Engineering and

Technology, Vol . 04, Issue: 01 , Jan-2015

41) J .Mantas, "An overview of character recognition methodologies”, Pattern

Recognition, vol. 19, no. 6, pp. 425-43 0, 1 986.

42) Rajean Plamondon, Fellow IEEE and Sargur N. Srihari, Fellow IEEE, “On-Line And

Off-Line Handwriting character Recognition: A Comprehensive Survey”, IEEE

TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE.

VOL. 22, NO. 1. JANUARY 2000

43) Amritha Sampath, Tripti C, Govindaru V, "Freeman code based online handwritten

character recognition for Malayalam using back propagation neural networks",

International journal on Advanced computing, Vol. 3, No. 4, pp. 51 - 58, July 2012.

24

44) Pradeep, E Shrinivasan and S.Himavathi, "Diagonal Based Feature Extraction for

Handwritten Alphabets Recognition System Using Neural Network", International

Journal of Computer Science & Information Technology (IJCSIT), vol. 3, No 1, Feb

2011.

45) Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah, "An Improved Zone Based

Hybrid Feature Extraction Model for Handwritten Alphabets Recognition Using Euler

Number", International Journal of Soft Computing and Engineering (ISSN: 2231 -

2307), Vol. 2, Issue 2, pp. 504-508, May 2012

46) He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image

recognition. In Proceedings of the IEEE conference on computer vision and pattern

recognition (pp. 770–778).

47) Krizhevsky, A., Sutskever, I., Hinton, & G. E. (2012). Imagenet classification with

deep convolutional neural networks. In Advances in neural information processing

systems (pp. 1097–1105).

48) R. Gonzalez, E. Woods, Digital Image Processing, 3rd edition , Prentice hall.

49) www.mathswork.com