The number of distance measurement methods like nearest neighbor, similarity, Linear-correlation, cross-correlation and hamming were used to find the distance between character. In this paper we propose a statistical based feature extraction approach for recognition of handwritten Marathi characters. These features are dependent on area, shape, orientation, perimeter and other variation in handwritten characters. 200 samples of each character from different writers have been collected and database is prepaid. 100 samples of each character were treated as training samples and average eccentricity, orientation and center mass of gravity features were evaluated for these training samples. A distance based approach is used to classify remaining 100 testing samples of each character. The results show the satisfactory performance rate.

Pre-processing stages of handwritten character recognition system.
a) Sample handwritten Marathi character, b) AB(m1) is major axis and AC is base line axis (m2).

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Handwritten Marathi Basic Character Recognition using Statistical

Method

Parshuram M. Kamble∗ and Ravindra S. Hegadi

Department of Computer Science, Solapur University, Solapur 413 255, Maharashtra, India.

e-mail: parshu1983@gmail.com, rshegadi@gmail.com

Abstract. The number of distance measurement methods like nearest neighbor, similarity, Linear-correlation,

cross-correlation and hamming were used to find the distance between character. In this paper we propose

a statistical based feature extraction approach for recognition of handwritten Marathi characters. These features

are dependent on area, shape, orientation, perimeter and other variation in handwritten characters. 200 samples

of each character from different writers have been collected and database is prepaid. 100 samples of each

character were treated as training samples and average eccentricity, orientation and center mass of gravity

features were evaluated for these training samples. A distance based approach is used to classify remaining 100

testing samples of each character. The results show the satisfactory performance rate.

Keywords: Character recognition, Classification, Perimeter, Eccentricity, Mass of character, Orientation.

Introduction

Optical character recognition is a type of computer software designed to translate image of handwritten or typewritten

text in to machine editable text by recognizing characters at high speeds one at a time. Handwritten character

detection and recognition in general have a list of related applications, such as information retrieval or automatic

indexing namely document indexing, content based image retrieval and postal address recognition application, which

further opens up the possibility for advanced system. Marathi language uses 63 phonemic letters, divided into three

groups namely swear (Vowels: 13 letters), Vyanjan (Consonants: 38 letters) and Ank (numbers: 10 digits) as well

as Modifiers (Diacritics: 12 letters) as shown in figure 2. Development of offline and Online OCR for Marathi

handwritten characters and numbers is challenging work for researchers because handwritten characters of each

person are mimetic. People recognize the handwrittencharacters easily but machine has some difficultyto do this task.

Few works are reported in literature for the recognition of Marathi and other Indian language characters and

numbers. S. B Patil and G. R. Sinha (2012) proposed the real time handwritten Marathi numerals recognition using

neural network. They used multilayer backward propagation for recognition purpose. Handwritten Devnagari character

Recognition model using neural Network was proposed by Gaurav Jaiswal (2014). In her paper author used zone and

count metric based feature extraction Algorithm. Multilayer feed forward neural network based classifier was used for

classification. Mustafa D. A. (2013)has proposed optical character recognition system for multi font English text using

DCT and wavelet transform. They used wavelet for feature vector construction and DCT based recognition method.

Accuracy for his proposed method was 96%. M. M. Kodabagi et al. , (2013) proposed character recognition of Kannada

text in scene images using neural network. proposed method uses zone wise horizontal and vertical profile based

features and Neural network classification for basic Kannada character recognition. R. S. Hegadi and P. M. Kamble

(2014) proposed recognition of Marathi handwritten numerals using multi-layer feed-forward neural network. In this

experiment they segmented each numeral from the document and it is resized to 7 × 5 pixels using cubic interpolation.

These resized numerals were converted into a vector with 35 values. These vector values were used as input to train

the neural network. Multilayer feed-forward neural network was used to classify handwritten Marathi numerals. The

algorithms accuracy is 97%. A template matching based approach was proposed by Hegadi R. S. (2011) for the

recognition of Kannada numerals. This method uses the correlation coefficient for matching the numeral. This method

∗Corresponding author

28 ©Elsevier Publications 2014.

Handwritten Marathi Basic Character Recognition using Statistical Method

Figure 1. Architecture of HOCR. Figure 2. Sample handwritten basic marathi character.

achieved an accuracy of 91%. In another work by Hegadi R. S. (2012) a multilayer feed-forward neural network is

used for the classification of printed Kannada numerals. In this work experimentation was carried out using all the

existing fonts of printed Kannada numerals and this algorithm could recognize all the numerals. Pisal and Kamble

(2012) proposed the probabilistic neural network classification to recognize Marathi Handwritten characters. In their

paper authors used water reservoir feature extraction for each character. After feature extraction probabilistic neural

network based classification technique is used to classify each character.

1. Proposed system Design

In this paper, we propose statistical based feature extraction and classification of Handwritten Marathi characters.

Architecture of proposed method as shown in figure 1.

In any handwritten character recognition problem training and testing are two important stages. In our work the

training stage consists of preparation of standard database for Marathi handwritten character images and extraction of

features from these training dataset. In testing stage Marathi handwritten character images are tested against trained

handwritten character images. In pre-processing stage the input image is segmented in to individual characters and

each character image is convertedinto binary form. Pre-processing of these character images is essential before feature

extraction stage. In feature extraction stage we extract a set of features such as centroi d, eccentricity, perimeter and

orientation for each and every character. These features are used in the recognition stage.

2. Data Collection, Pre-Processing and Segmentation

2.1 Data collection

200 sets of handwritten Marathi character samples were collected randomly from different persons of different age

group, at different time and were used as dataset for the proposed experimentation. These datasets were collected

on A4 size white blank paper and were scanned using Umax Astra 5600 scanner with 300dpi. After this process all

sample were stored in JPEG image file format.

2.2 Pre-Processing

Pre-processing plays an important role in handwritten character Recognition as in any other pattern recognition. The

figure 3 show stages in pre-processing and segmentation of a characters from a input document image. The main

objectives of pre-processing are binarization of input image, noise reduction, connect broken tiny character, edge

detection, region filling, normalization and segmentation. The binarization of image is done by applying thresholding

technique. Thresholding refers to the conversion of gray-scale image to the Binary image. This process converts

the image into two components, namely, the object component and background. The object component contains the

characters and background contains the noise and other unwanted information. Handwritten character shows various

undesirable effects like small unwanted strokes, gaps or breaks which occur due to binarization. Many times when

©Elsevier Publications 2014. 29

Parshuram M. Kamble and Ravindra S. Hegadi

Figure 3. Pre-processing stages of handwritten character recognition system.

Figure 4. a) Sample handwritten Marathi character, b) AB(m 1) is major axis and AC is base line axis (m 2).

a character is handwritten, it exhibits lesser width at the curvature than at other part of the character. This point is more

likely to break during binarization. Hence a 3 × 3 averaging operator is implemented before binarization, which blurs

the image resulting into bridging small gaps and retaining the actual shape of the character. It also removes pepper

noise. The unwanted strokes occur more often between the pen lifting and placing points and their occurrence depend

upon the writing style and the ink viscosity. These strokes may result into unwanted feature detection after binarization.

In order to avoid this, the binarized image should be cleaned. This is done by using morphological opening operator.

Morphological opening removes thin protrusions, breaks thin connectionsand smoothes the object contour.

In the proposed work, marathi basic characters are written on plain paper. The characters are written in such way

that they do not overlap. They are segmented using bounding box. The character separated are further cropped and

then passed to normalization. The cropped character are in different size, because handwritten style of each writer is

dissimilar. Normalization is applied on each character to bring all the character to uniform size. then these character is

passed to the feature extraction process during Training and Testing time.

3. Feature Extraction

3.1 Orientation

Angle of orientation (in degrees ranging from − 90 to 90 degrees) is the angle between major axis of the oval which

covers the character and x -axis, as shown in figure 4. Solid blue line are axes of the ellipse and red dots are the foci

of covered character region of ellipse. The orientation [8] is the angle between the horizontal line and the major axis,

which is given by in figure 4. The angle theta (θ ) is calculated by using equation 1. In this equation the major axis is

m1andthem 2 is the base line.

tan(θ) =m 1−m2

1+m 1m 2(1)

3.2 Centriod

The two co-ordinates ¯ xand ¯ yspecifies the center of mass for character region. The first and second element of Centriod

is the horizontal and vertical co-ordinate of the character region. Figure 5 illustrates the Centriod [8] for the sample

character region. The region consist of white pixels; the red dot is Centriod.

¯ x=size of Mth element of character/2 and ¯ y=size of Nth element of character/2

30 ©Elsevier Publications 2014.

Handwritten Marathi Basic Character Recognition using Statistical Method

Figure 5. Sample handwritten Marathi character,

b) Red dot is the centriod of character. Figure 6. a) Sample handwritten Marathi

character, b) Border of character.

3.3 Peri meter

It is the distance around the boundary of the Character region. The perimeter [8] has been calculated by using distance

between each adjoining pair of pixels around the border of the region of the character. The following figure 6 shows

the pixels included in the perimeter calculation for this Character.

3.4 Eccentricity

Shape, size and orientation of Marathi characters are heterogynous. Generally, shape of handwritten Marathi vowels

is like an oval shape. We used Eccentricity [8] of character as one of the feature for our work. Eccentricity is the ratio

of major axis and minor axis of ellipse which covers the entire character as given by equation 2. In this equation AB

is the major axis and CD is the minor axis.

Ecentricity =AB

CD (2)

Eccentricity is calculated for all characters with connected axes regions. In figure 7 the red line is the ellipse region of

Handwritten Marathi letter and the blue lines AB is the major axis and CD is minor axis.

3.5 Area of character

Figure 7. a) Sample handwritten Marathi

character, b) Major axis and minor axis.

The mass of character is the total number of white pixels in the binarized

character. The total number of white pixels are counted in each character

to obtain its mass value. After pre processing character is normalized in

standard 50 by 70 size then area [8] is calculated.

4. Classification

We computed the four features namely, total mass of character,

Centriod, eccentricity and orientation, for 100 set of Basic Marathi Handwritten Characters and stored same in

database. The above said features were computed for different sets of training samples and distance between features of

each character in the stored database is computed against the training dataset. Figure 8 shows a GUI where a character

image is read and the features are computed and a label is displayed for the class to which that character belongs.

Figure 8. GUI showing the labe of Marathi handwritten character.

©Elsevier Publications 2014. 31

Parshuram M. Kamble and Ravindra S. Hegadi

Tabl e 1 . Average values of features 100 training samples of each character and accuracy rate of

100 testing samples of each character.

5. Result and Discussion

The Experimentation is carried out using Matlab 7.0 tool. 200 different set of each handwritten characters of Marathi

language were used for this experimentation, out of which 100 sets of each characters were used as training samples

and 100 sets of each characters as testing samples. From t he 100 training sets of each characters the four features

32 ©Elsevier Publications 2014.

Handwritten Marathi Basic Character Recognition using Statistical Method

namely, eccentricity, orientation and mass of character, and perimeter were obtained and average value is computed for

each character, which is shown in table 1. Again these features were computed for each character from the 100 sets of

testing sample and distance is computed between each of these character and the values obtained for training samples.

Based on the smallest distance the classification is done. In tabel 1 shows The classification accuracy for each of these

characters. It can be noticed that the character such as , , , , , , , , and were classified with very high rate

accuracy. where our Technique has performed very poor for the character like , , and . overall FAR of proposed

algorithm was 15.52% due to the fact that the a few writers written equivalent shape in character. Hence in many cases

this character may be falsie classified as . The overall accuracy of proposed algorithm was 94.38%.

6. Conclusion

In this paper we have proposed a statistical feature extraction on Marathi Handwritten basic Character Recognition.

We can apply two stage recognition approaches to improve the performance of the scheme. The main characteristics

of the Marathi characters is their shapes which are mostly formed with more curves. Most of the failures in recognition

are due to either characters with sharp edges and corners, or writing inappropriate style of a characters making it as

unknown characters. The post processing may improve the performance which we will undertake in our feature work.

References

[1] S. B. Patil and G. R. Sinha, "Real Time Handwritten Marathi Numerals Recognition using Neural Network", Int. Jr. Info. Tech.

Comp. Sci., pp. 76–81, (2012).

[2] D. A. Mustafa, "Optical Character Recognition (OCR) System For Multifint English Texts using DCT & Wavelet Transform",

Int. Jr. Comp. Engg, and Tech., pp. 48–61, (2013).

[3] R. S. Hegadi and P. M. Kamble, "Recognition of Marathi Handwritten Numerals Using Multi-layer Feed-Forward Neural

Network", IEEE Explore, WCCT pp. 21–24, (2014).

[4] M. M. Kodabagi, S. A. Angadi and C. R. Shivanagi, "Character Recognition of Kannada Text in Scence Images using Neural

Network", Int. Jr. Graphics and Multimedia, vol. 4, Issue 1, pp. 09–19, (2013).

[5] R. S. Hegadi, "Classification of Kannada Numerals using Multi-layer Neural Network", Adv. in Int. Sy. and Comp. , vol. 174,

pp. 963–968, (2012).

[6] R. S. Hegadi, "Template Matching Approach for Printed Kannada Numeral Recognition", Int. Conf. Comp. Int. Info. Tech. ,

pp. 480–483, (2011).

[7] T. B. Pisal and P. M. Kamble, "Marathi Character Recognition by using Probabilistic Neural Network Classification", Int.

Jr. Comp. Sci. Info. Tech., pp. 66–63, (2012).

[8] http://www.mathworks.in/help/images/ref/regionprops.html.

[9] Gaurav Jaiswal, "Handwritten Devanagari Character Recognition Model using Neural Network", Int. Jr. of. Engg. Dev.

Research., vol. 2, Issue 1, pp. 901–906, (2014).

©Elsevier Publications 2014. 33

... Handwritten character detection and recognition in general have a list of related applications, such as information retrieval or automatic indexing, such as document indexing, content based image retrieval and postal address recognition application, which further opens up the possibility for advanced system. Marathi language uses 63 phonemic letters, divided into three groups namely swaar (Vowels: 12 letters, as shown in Figure 1 , Vyanjan (Consonants: 38 letters), Ankh (numbers: 10 digits) and Modifiers (Diacritic: 12 letters) [1], [2]. Development of off-line and On-line OCR for (MHC) Marathi handwritten characters is challenging work for researchers because handwriting of each person are mimetic. ...

... A similar work was proposed by Kamble et. al. [1] in which features such as eccentricity, orientation and mass of characters were extracted and minimum distance classifier was used for classification. In this work authors calculated the eccentricity, orientation and mass of character feature, minimum distance was used for classification. ...

... Eccentricity is given by age into two components, namely, the object component and background. The object component contains the characters M ax axes Eccentricity = M in axes (1) and background contains the noise and other unwanted information. During the scanning of input handwritten Marathi ...

... In scanned document may contain noise, which may affect the recognition rate. Hence a noise removal technique using statically smoothing filter [2,1] is applied to reduce the effect of noise. In the next step individual character is segmented from documents using bounding box based character segmentation method [2]. ...

... In the next step individual character is segmented from documents using bounding box based character segmentation method [2]. The variation in the size of the character image is normalized to 42 × 42 pixel dimension [1]. Binarization operation is performed on grey scale image using Thresholding function. ...

Recognition of handwritten Marathi characters is challenging field in image processing and character recognition system. In this proposed method, we extract futures from handwritten Marathi characters using multiwavelet and connected pixel based feature extraction methods. Classification of handwritten Marathi characters is done by using the bagged tree and Quadratics discriminate classifier. Before feature extraction and classification, handwritten character images were enhanced using preprocessing techniques. Experimental analysis and performance of proposed methods were measured using five-fold cross-validation. The proposed model reduces the false acceptance during recognition of handwritten Marathi characters.

... Optical character detection and recognition is used in various applications, such as document indexing, postal address recognition, number plate recognition, information retrieval and office automation. Marathi language is belongs to Devanagari script, it have 63 phonic letters, further they subdivided into three groups namely (vowels: 12 letters, as shown in Fig. 1, Vyanjan (Consonants: 38 letters), Ankh (numbers: 10 digits) and Modifiers (Diacritic: 12 letters) [6,8]. ...

... In this paper they used 17 geometric features based on pixel connectivity, line direction, lines, image area, perimeter, orientation etc. and 5 discriminant functions namely, quadratic linear, Mahalanobis and bi-quadratic distance were used for classification. A similar work was proposed by Kamble et al. [8] in which features such as eccentricity, orientation and mass of characters were extracted and minimum distance classifier was used for classification. In another work by Kale et al. [6] Zernike moment based feature extraction for handwritten Devanagari compound character recognition. ...

Robust handwritten Marathi character recognition is essential to the proper function in document analysis field. Many researches in OCR have been dealing with the complex challenges of the high variation in character shape, structure and document noise. In proposed system, noise is removed by using morphological and thresholding operation. Skewed scanned pages and segmented characters are corrected using Hough Transformation. The characters are segmented from scanned pages by using bounding box techniques. Size variation of each handwritten Marathi characters are normalized in 40 \(\times \) 40 pixel size. Here we propose feature extraction from handwritten Marathi characters using connected pixel based features like area, perimeter, eccentricity, orientation and Euler number. The modified k-nearest neighbor (KNN) and SVM algorithm with five fold validation has been used for result preparation. The comparative accuracy of proposed methods are recorded. In this experiment modified SVM obtained high accuracy as compared with KNN classifier.

... In literature we observe, the accuracy of OCR is less due to the noise and broken characters [13]. The sources of noise in text document images are optical device, quality of printing device (pen, paper and printer) and pre-processing techniques [6]. In OCR system, generally various pre-processing techniques are applied on text document image, finally we got characters in good or few characters in bad shape. ...

This article proposes an minimum distance based edge linking algorithms for handwritten character images. Improvement of performance for machine recognition is challenging task due to noise and degraded input images. In the proposed system we enhance the recognition rate of object reconstruction for broken edges by using edge linking. Such edges of objects are reconstructed by using novel Distance based Edge Linking (DEL) approach. Developed new benchmark approach is fill the gaps between nearest edge segment of Binary image map (BIM). We obtain state-of-art performance of proposed system on character recognition (CR) using two datasets MNIST and ISI.

... Handwritten character recognition system with higher recognition rates may be aptly suitable for several applications including postal/parcel address recognition, document reading and conversion of any handwritten document into text form. The number of distance measurement methods like nearest neighbour, linear-correlation, cross-correlation and hamming distance is used to find the distance between characters (Parshuram and Ravindra, 2014). Most of the failures in recognition are due to dealing with characters having either sharp edges and corners, or inappropriate writing style of characters. ...

... Handwritten character recognition system with higher recognition rates may be aptly suitable for several applications including postal/parcel address recognition, document reading and conversion of any handwritten document into text form. The number of distance measurement methods like nearest neighbour, linear-correlation, cross-correlation and hamming distance is used to find the distance between characters (Parshuram and Ravindra, 2014). Most of the failures in recognition are due to dealing with characters having either sharp edges and corners, or inappropriate writing style of characters. ...

... Character recognition of handwritten and printed text images is an important task in pattern recognition as many applications depend on the correct character recognition. Examples of applications of OCR are cheque sorting, number plate recognition and automatic form filling ( Kamble and Hegadi, 2014). The OCR problem is usually divided into several steps, similar to other classification problems: detection of object location, preprocessing, representation, feature extraction and classification ( Niknam and Kaka, 2012). ...

In this work we show that the sparse autoencoder based feature extraction method in combination with deep neural network based classifier can produce enhanced results when applied to Marathi character recognition. However, in pattern recognition tasks, such as the optical character recognition problem, it is difficult to directly convert handwritten character document image into its constituent character data. Here, we proposed classification of handwritten Marathi character using Deep Neural Network (DNN), which is based on two key concepts: autoencoder features and deep neural network architectures. The proposed model extracts the valid patches from character zones using voting schemes. These extracted patches are arranged in row vector and further fed into DNN for training and testing. We evaluated our approach on handwritten numeral and character of two cross datasets. The proposed classification technique has shown higher performance results over both datasets.

... The main guerdon of such an attempt was not only human studying but also the probability of systematic application in which handwritten and/or printed character available on document has to be transformed into machine learning design [5,6]. Automatic character recognition of printed and handwritten information in various commercial departments such as libraries, post offices, banks, and publishing houses on documents like cheques, envelopes, other manuscripts and application forms of different filed has a variety of applications [7]. Now a days, Optical Character Recognition (OCR) and artificial neural network (ANN) is used for character verification and classifier respectively. ...

  • Sunil Kumar
  • Krishan Kumar Krishan Kumar
  • Rahul Kumar Mishra

Nowadays, scene text recognition has become an important emerging area of research in the field of image processing. In image processing, character recognition boosts the complexity in the area of Artificial Intelligence. Character recognition is not easy for computer programs in comparison to humans. In the broad spectrum of things, it may consider that recognizing patterns is the only thing which humans can do well and computers cannot. There are many reasons including various sources of variability, hypothesis and absence of hard-and-fast rules that define the appearance of a visual character. Hence; there is an unavoidable requirement for heuristic deduction of rules from different samples. This review highlights the superiority of artificial neural networks, a popular area of Artificial Intelligence, over various other available methods like fuzzy logic and genetic algorithm. In this paper, two methods are listed for character recognition – offline and online. The ―Offline‖ methods include Feature Extraction, Clustering, and Pattern Matching. Artificial neural networks use the static image properties. The online methods are divided into two methods, k-NN classifier and direction based algorithm. Thus, the scale of techniques available for scene text recognition deserves an admiration. This review gives a detail survey of use of artificial neural network in scene text recognition.

  • Ranjana S. Zinjore
  • Rakesh Ramteke Rakesh Ramteke

India is a Multistate- Multilingual country. Most of the people in India used their state official language and English is treated as a binding language used for form filling or some official work. So there is a need to create a system which will convert the handwritten bilingual document into digitized form. This paper aims at development of reader system for handwritten bilingual (Marathi-English) documents by recognizing words. This facilitates many applications such as Natural language processing, School, Society, Banking, post office and Library automation. The proposed system is divided into two phases. The first phase focuses on recognition of handwritten bilingual words using two different feature extraction methods including combination of structural and statistical method and Histogram of Oriented Gradient Method. K-Nearest Neighbor classifier is used for recognition. This classifier gives 82.85% recognition accuracy using Histogram of Oriented Gradient method. The dataset containing 4390 words collected from more than 100 writers. The second phase focuses on digitization and transliteration of recognized words and conversion of transliterated text into speech, which is useful in the society for visually impaired people.

Marathi is one of the ancient Indian languages majorly spoken in the state of Maharashtra. Marathi is one of the Devanagari script and the literals and numerals are almost similar to Hindi. Recognition of handwritten Marathi numerals is quite challenging task because people have the practice of writing these numerals in variant ways. In this work we have presented a method to recognize the handwritten Marathi numerals using multilayer feed-forward neural network. The scanned document image is pre-processed to eliminate the noise and care is taken to link the broken characters. Each numeral is segmented from the document and it is resized to 7 × 5 pixels using cubic interpolation. While resizing a technique is used to provide better representation for every pixel in segmented numeral. This resized numeral is converted into a vector with 35 values before inputting it to the neural network. We have used 100 sets containing 1000 numerals for this experimentation, of which 50 sets are used for training the network and 50 sets for the testing purpose. The overall recognition rate of the proposed method is 97%.

Character recognition is an important task in biometrics. This paper uses neural network for real time handwritten Marathi numerals recognition. We have taken 150 online Marathi numerals written in different styles by 10 different persons. Out of these, 50 numerals were used for training purpose and another 100 numerals were used for recognition purpose. The numerals undergo the preprocessing steps using image processing techniques and after character digitization it is further subjected to the multilayer backward propagation neural network for recognition purpose. The proposed research work gives recognition accuracy from 97% and to 100% for the different resolution of input vector.

  • Dr. Mustafa Dhiaa Al-Hassani Dr. Mustafa Dhiaa Al-Hassani

Optical Character Recognition (OCR) is a type of computer software designed to translate images of handwritten or typewritten text (usually captured by a scanner or a camera) into machineeditable text by recognizing characters at high speeds one at a time. OCR began as a field of research in pattern recognition, artificial intelligence and machine vision. It is becoming more and more important in the modern world according to economic reasons and business requirements. It helps humans ease their jobs and solve more complex problems by eliminating the time-consuming spent by human operators to re-type the documents and reduce error-prone processes. The presence of any type of noise or a combination of them can severely degrade the performance of OCR system. Though, a number of preprocessing techniques are considered in the present work in order to improve the obtained accuracy of the recognized text. An OCR system for 3185 training samples and 13650 testing samples is presented for multi-font English texts. Experiments have shown that wavelet features produce better recognition rates 96% than DCT features 92%. An improvement overall recognition rates (about 3%) are obtained after classifying characters according to the proportion of Height to Width feature to produce 99% for wavelet and 95% for DCT.

  • Ravindra S Hegadi Ravindra S Hegadi

A simple multilayer feed forward neural network based classification of handwritten as well as printed Kannada numerals is presented in this paper. A feed forward neural network is an artificial neural network where connections between the units do not form a directed cycle. Here four sets of Kannada numerals from 0 to 9 are used for training the network and one set is tested using the proposed algorithm. The input scanned document image containing Kannada numerals is binarized and a negative transformation is applied followed by noise elimination. Edge detection is carried out and then dilation is applied using 3 × 3 structuring element. The holes present in this image are filled. Every image is then segmented out forming 50 segmented images each containing one numeral, which is then resized. A multilayer feed forward neural network is created and this network is trained with 40 neural images. Then testing has been performed over ten numeral images. The proposed algorithm could perfectly able to classify and recognize the printed numerals with different fonts and hand written numerals.

  • Ravindra S Hegadi Ravindra S Hegadi

In this paper a simple template matching method based on correlation coefficient is proposed to recognize the printed Kannada numerals. Here the scanned printed Kannada numeral documents are preprocessed and each numeral is extracted by first performing the line segmentation and then segmenting each numeral from the segmented line. Each segmented object is resized to a predefined size. The correlation coefficient is computed between the image under consideration and stored numeral image data. A high value of correlation coefficient will indicate the successful match. The proposed algorithm is invariant to size of the numerals since segmented objects are resized in the preprocessing stage. Experimentation is carried out on 30 different fonts of Kannada numerals generated from Nudi 4.0 software. The results are encouraging.

Character Recognition of Kannada Text in Scence Images using Neural Network

  • M M Kodabagi
  • S A Angadi
  • C R Shivanagi

M. M. Kodabagi, S. A. Angadi and C. R. Shivanagi, "Character Recognition of Kannada Text in Scence Images using Neural Network", Int. Jr. Graphics and Multimedia, vol. 4, Issue 1, pp. 09-19, (2013).

Marathi Character Recognition by using Probabilistic Neural Network Classification

  • T B Pisal
  • P M Kamble

T. B. Pisal and P. M. Kamble, "Marathi Character Recognition by using Probabilistic Neural Network Classification", Int. Jr. Comp. Sci. Info. Tech., pp. 66-63, (2012).

Handwritten Devanagari Character Recognition Model using Neural Network

  • Gaurav Jaiswal

Gaurav Jaiswal, "Handwritten Devanagari Character Recognition Model using Neural Network", Int. Jr. of. Engg. Dev. Research., vol. 2, Issue 1, pp. 901-906, (2014).