Recognition of Persian handwritten characters has been considered as a significant field of research for
the last few years under pattern analysing technique. In this paper, a new approach for robust handwritten
Persian numerals recognition using strong feature set and a classifier fusion method is scrutinized to
increase the recognition percentage. For implementing the classifier fusion technique, we have considered
k nearest neighbour (KNN), linear classifier (LC) and support vector machine (SVM) classifiers. The
innovation of this tactic is to attain better precision with few features using classifier fusion method. For
evaluation of the proposed method we considered a Persian numerals database with 20,000 handwritten
samples. Spending 15,000 samples for training stage, we verified our technique on other 5,000 samples,
and the correct recognition ratio achievedapproximately 99.90%. Additional, we got 99.97% exactness
using four-fold cross validation procedure on 20,000 databases.
In This paper we presented new approach for cursive Arabic text recognition system. The objective is to propose methodology analytical offline recognition of handwritten Arabic for rapid implementation.The first part in the writing recognition system is the preprocessing phase is the preprocessing phase to prepare the data was introduces and extracts a set of simple statistical features by two methods : from a window which is sliding long that text line the right to left and the approach VH2D (consists in projecting every character on the abscissa, on the ordinate and the diagonals 45° and 135°) . It then injects the resulting feature vectors to Hidden Markov Model (HMM) and combined the two HMM by multi-stream approach.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Devnagari document segmentation using histogram approachVikas Dongre
Document segmentation is one of the critical phases in machine recognition of any language. Correct
segmentation of individual symbols decides the accuracy of character recognition technique. It is used to
decompose image of a sequence of characters into sub images of individual symbols by segmenting lines and
words. Devnagari is the most popular script in India. It is used for writing Hindi, Marathi, Sanskrit and
Nepali languages. Moreover, Hindi is the third most popular language in the world. Devnagari documents
consist of vowels, consonants and various modifiers. Hence proper segmentation of Devnagari word is
challenging. A simple histogram based approach to segment Devnagari documents is proposed in this paper.
Various challenges in segmentation of Devnagari script are also discussed.
Image to Text Converter PPT. PPT contains step by step algorithms/methods to which we can convert images in to text , specially contains algorithms for images which contains human handwritting, can convert writting in to text, img to text.
A NOVEL FEATURE SET FOR RECOGNITION OF SIMILAR SHAPED HANDWRITTEN HINDI CHARA...cscpconf
The growing need of handwritten Hindi character recognition in Indian offices such as passport, railway etc, has made it a vital area of research. Similar shaped characters are more prone to misclassification. In this paper four Machine Learning (ML) algorithms namely Bayesian Network, Radial Basis Function Network (RBFN), Multilayer Perceptron (MLP), and C4.5 Decision Tree are used for recognition of Similar Shaped Handwritten Hindi Characters (SSHHC) and their performance is compared. A novel feature set of 85 features is generated on the basis of character geometry. Due to the high dimensionality of feature vector, the classifiers can be computationally complex. So, its dimensionality is reduced to 11 and 4 using Correlation-Based (CFS) and Consistency-Based (CON) feature selection techniques respectively. Experimental results show that Bayesian Network is a better choice when used with CFS while C4.5 gives better performance with CON features.
In This paper we presented new approach for cursive Arabic text recognition system. The objective is to propose methodology analytical offline recognition of handwritten Arabic for rapid implementation.The first part in the writing recognition system is the preprocessing phase is the preprocessing phase to prepare the data was introduces and extracts a set of simple statistical features by two methods : from a window which is sliding long that text line the right to left and the approach VH2D (consists in projecting every character on the abscissa, on the ordinate and the diagonals 45° and 135°) . It then injects the resulting feature vectors to Hidden Markov Model (HMM) and combined the two HMM by multi-stream approach.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Devnagari document segmentation using histogram approachVikas Dongre
Document segmentation is one of the critical phases in machine recognition of any language. Correct
segmentation of individual symbols decides the accuracy of character recognition technique. It is used to
decompose image of a sequence of characters into sub images of individual symbols by segmenting lines and
words. Devnagari is the most popular script in India. It is used for writing Hindi, Marathi, Sanskrit and
Nepali languages. Moreover, Hindi is the third most popular language in the world. Devnagari documents
consist of vowels, consonants and various modifiers. Hence proper segmentation of Devnagari word is
challenging. A simple histogram based approach to segment Devnagari documents is proposed in this paper.
Various challenges in segmentation of Devnagari script are also discussed.
Image to Text Converter PPT. PPT contains step by step algorithms/methods to which we can convert images in to text , specially contains algorithms for images which contains human handwritting, can convert writting in to text, img to text.
A NOVEL FEATURE SET FOR RECOGNITION OF SIMILAR SHAPED HANDWRITTEN HINDI CHARA...cscpconf
The growing need of handwritten Hindi character recognition in Indian offices such as passport, railway etc, has made it a vital area of research. Similar shaped characters are more prone to misclassification. In this paper four Machine Learning (ML) algorithms namely Bayesian Network, Radial Basis Function Network (RBFN), Multilayer Perceptron (MLP), and C4.5 Decision Tree are used for recognition of Similar Shaped Handwritten Hindi Characters (SSHHC) and their performance is compared. A novel feature set of 85 features is generated on the basis of character geometry. Due to the high dimensionality of feature vector, the classifiers can be computationally complex. So, its dimensionality is reduced to 11 and 4 using Correlation-Based (CFS) and Consistency-Based (CON) feature selection techniques respectively. Experimental results show that Bayesian Network is a better choice when used with CFS while C4.5 gives better performance with CON features.
Dimensionality Reduction and Feature Selection Methods for Script Identificat...ITIIIndustries
The goal of this research is to explore effects of dimensionality reduction and feature selection on the problem of script identification from images of printed documents. The kadjacent segment is ideal for this use due to its ability to capture visual patterns. We have used principle component analysis to reduce the size of our feature matrix to a handier size that can be trained easily, and experimented by including varying combinations of dimensions of the super feature set. A modular
approach in neural network was used to classify 7 languages – Arabic, Chinese, English, Japanese, Tamil, Thai and Korean.
A PREPROCESSING MODEL FOR HAND-WRITTEN ARABIC TEXTS BASED ON VORONOI DIAGRAMSijcsit
In this paper, a preprocessing model for hand-written Arabic text on the basis of the Voronoi Diagrams (VDs) is presented and discussed. The proposed VD-based pre-processing model consists of five stages: a preparatory stage, page segmentation, thinning, baseline estimation, and slanting correction. In the preparatory stage, the text image is converted via VDs into a group of geometrical forms that consist of edges and vertices that are used to create the other stages of the proposed model. This stage consists of
four main processes: binarization, edge extraction and contour tracking, sampling, and point-VD construction. The second stage is the page segmentation stage based on the VD area. In the third stage, an efficient method for text structuring (that is, thinning) is presented. In the fourth stage, a novel baseline
based VD method is presented. In the fifth stage, an efficient technique for slanting detection and correction is proposed and discussed.
SEGMENTATION OF CHARACTERS WITHOUT MODIFIERS FROM A PRINTED BANGLA TEXTcscpconf
Optical Character Recognition (OCR) is one of the fundamental research areas of image processing and pattern recognition field. The performance accuracy of an OCR system depends on the proper segmentation of the characters. This paper is concerned with the segmentation of printed bangla characters without modifiers for optical character recognition (OCR) system. The basic steps needed for developing an OCR system also have been discussed.
Offline Signiture and Numeral Recognition in Context of ChequeIJERA Editor
Signature is considered as one of the biometrics. Signature Verification System is required in almost all places where it is compulsory to authenticate a person or his/her credentials to proceed further transaction especially when it comes to bank cheques. For this purpose signature verification system must be powerful and accurate. Till date various methods have been used to make signature verification system powerful and accurate. Research here is related to offline signature verification. Shape Contexts have been used to verify whether 2 shapes are similar or not. It has been used for various applications such as digit recognition, 3D Object recognition, trademark retrieval etc. In this paper we present a modified version of shape context for signature verification on bank cheques using K-Nearest Neighbor classifier.
An effective approach to offline arabic handwriting recognitionijaia
Segmentation is the most challenging part of the Arabic handwriting recognition, due to the unique
characteristics of Arabic writing that allows the same shape to denote different characters. In this paper,
an off-line Arabic handwriting recognition system is proposed. The processing details are presented in
three main stages. Firstly, the image is skeletonized to one pixel thin. Secondly, transfer each diagonally
connected foreground pixel to the closest horizontal or vertical line. Finally, these orthogonal lines are
coded as vectors of unique integer numbers; each vector represents one letter of the word. In order to
evaluate the proposed techniques, the system has been tested on the IFN/ENIT database, and the
experimental results show that our method is superior to those methods currently available.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Study on a Hybrid Segmentation Approach for Handwritten Numeral Strings in Fo...inventionjournals
This paper presents a hybrid approach to segment single- or multiple-touching handwritten numeral strings in form document, the core of which is the combined use of foreground, background and recognition analysis. The algorithm first located some feature points on both the foreground and background skeleton images containing connected numeral strings in form document. Possible segmentation paths were then constructed by matching these feature points, with an unexpected benefit of removing useless strokes. Subsequently, all these segmentation paths were validated and ranked by a recognition-based analysis, where a well-trained two-stage classifier was applied to each separated digit image to obtain its reliability. Finally, by introducing a locally optimal strategy to accelerate the recognition process, the top ranked segmentation path survived to help make a decision on whether to accept or not. Experimental results show that the proposed method can achieve a correct segmentation rate of 96.2 percent on a large dataset collected by our own.
DEVNAGARI DOCUMENT SEGMENTATION USING HISTOGRAM APPROACHijcseit
Document segmentation is one of the critical phases in machine recognition of any language. Correct
segmentation of individual symbols decides the accuracy of character recognition technique. It is used to
decompose image of a sequence of characters into sub images of individual symbols by segmenting lines and
words. Devnagari is the most popular script in India. It is used for writing Hindi, Marathi, Sanskrit and
Nepali languages. Moreover, Hindi is the third most popular language in the world. Devnagari documents
consist of vowels, consonants and various modifiers. Hence proper segmentation of Devnagari word is
challenging. A simple histogram based approach to segment Devnagari documents is proposed in this paper.
Various challenges in segmentation of Devnagari script are also discussed.
SCENE TEXT RECOGNITION IN MOBILE APPLICATION BY CHARACTER DESCRIPTOR AND STRU...Cheriyan K M
In text detection, our
previously proposed algorithms are applied to obtain text regions
from scene image. First, we design a discriminative character
descriptor by combining several state-of-the-art feature detectors
and descriptors. Second, we model character structure at each
character class by designing stroke configuration maps.
Hierarchical Approach for Total Variation Digital Image InpaintingIJCSEA Journal
The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consum ing process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.
Nowadays character recognition has gained lot of attention in the field of pattern recognition due to its application in various fields. It is one of the most successful applications of automatic pattern recognition. Research in OCR is popular for its application potential in banks, post offices, office automation etc. HCR is useful in cheque processing in banks; almost all kind of form processing systems, handwritten postal address resolution and many more. This paper presents a simple and efficient approach for the implementation of OCR and translation of scanned images of printed text into machine-encoded text. It makes use of different image analysis phases followed by image detection via pre-processing and post-processing. This paper also describes scanning the entire document (same as the segmentation in our case) and recognizing individual characters from image irrespective of their position, size and various font styles and it deals with recognition of the symbols from English language, which is internationally accepted.
An improved double coding local binary pattern algorithm for face recognitioneSAT Journals
Abstract A human face conveys a lot of information about the identity and emotional state of the person. So now a day’s face recognition has become an interesting and challenging problem. Face recognition plays a vital role in many applications such as authenticating a person, system security, verification and identification for law enforcement and personal identification among others. So our research work mainly consists of three parts, namely face representation, feature extraction and classification. The first part, Face representation represents how to model a face and check which algorithms can be used for detection and recognition purpose. In the second phase i.e. feature extraction phase we compute the unique features of the face image. In the classification phase the computed DLBP face image is compared with the images from the database. In our research work, we use Double Coding Local Binary Patterns to evaluate face recognition which concentrate over both the shape and texture information to represent face images for person independent face recognition. The face area is firstly cut into small regions from which Local Binary Patterns (LBP), then we compute histograms to generate LBP image then we compute single oriented mean image from which we again compute histogram values small regions and at last concatenated into a single feature vectors and generate D-LBP image. This feature are used for the representation of the face and to measure similarities between images. Keywords: local binary pattern (LBP), double coding local binary pattern (D-LBP), features extraction, classification, pattern recognition, histogram, feature vector.
Role Model of Graph Coloring Application in Labeled 2D Line Drawing ObjectWaqas Tariq
Several researches had worked on the development of sketch interpreters. However, very few of them gave a complete cycle of the sketch interpreter which can be used to transform an engineering sketch to a valid solid object. In this paper, a framework of the complete cycle of the sketch interpreter is presented. The discussion in this paper will stress on the usage of line labeling and graph coloring application in the validation of two dimensional (2D) line drawing phase. Both applications are needed to determine whether the given 2D line drawing represent possible or impossible object. In 2008, previous work by Matondang et al., has used line labeling algorithm to validate several 2D line drawings. However, the result shows that line labeling algorithm is not sufficient, as the algorithm does not have a validation technique for the result. Therefore, in this research study, it is going to be shown that if a 2D line drawing is valid as a possible object by using the line labeling algorithm, then it can be colored using graph coloring concept with a determine-able minimum numbers of color needed. This is equal in vice versa. The expected output from this phase is a valid-labeled of 2D line drawing with different colors at each edge and ready for the reconstruction phase. As a preliminary result, a high programming language MATLAB R2009a and several primitive 2D line drawings has been used and presented in this paper to test the graph coloring concept in labeled 2D line drawing.
Improvement of the Recognition Rate by Random ForestIJERA Editor
In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures
Improvement oh the recognition rate by random forestYoussef Rachidi
In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.
Dimensionality Reduction and Feature Selection Methods for Script Identificat...ITIIIndustries
The goal of this research is to explore effects of dimensionality reduction and feature selection on the problem of script identification from images of printed documents. The kadjacent segment is ideal for this use due to its ability to capture visual patterns. We have used principle component analysis to reduce the size of our feature matrix to a handier size that can be trained easily, and experimented by including varying combinations of dimensions of the super feature set. A modular
approach in neural network was used to classify 7 languages – Arabic, Chinese, English, Japanese, Tamil, Thai and Korean.
A PREPROCESSING MODEL FOR HAND-WRITTEN ARABIC TEXTS BASED ON VORONOI DIAGRAMSijcsit
In this paper, a preprocessing model for hand-written Arabic text on the basis of the Voronoi Diagrams (VDs) is presented and discussed. The proposed VD-based pre-processing model consists of five stages: a preparatory stage, page segmentation, thinning, baseline estimation, and slanting correction. In the preparatory stage, the text image is converted via VDs into a group of geometrical forms that consist of edges and vertices that are used to create the other stages of the proposed model. This stage consists of
four main processes: binarization, edge extraction and contour tracking, sampling, and point-VD construction. The second stage is the page segmentation stage based on the VD area. In the third stage, an efficient method for text structuring (that is, thinning) is presented. In the fourth stage, a novel baseline
based VD method is presented. In the fifth stage, an efficient technique for slanting detection and correction is proposed and discussed.
SEGMENTATION OF CHARACTERS WITHOUT MODIFIERS FROM A PRINTED BANGLA TEXTcscpconf
Optical Character Recognition (OCR) is one of the fundamental research areas of image processing and pattern recognition field. The performance accuracy of an OCR system depends on the proper segmentation of the characters. This paper is concerned with the segmentation of printed bangla characters without modifiers for optical character recognition (OCR) system. The basic steps needed for developing an OCR system also have been discussed.
Offline Signiture and Numeral Recognition in Context of ChequeIJERA Editor
Signature is considered as one of the biometrics. Signature Verification System is required in almost all places where it is compulsory to authenticate a person or his/her credentials to proceed further transaction especially when it comes to bank cheques. For this purpose signature verification system must be powerful and accurate. Till date various methods have been used to make signature verification system powerful and accurate. Research here is related to offline signature verification. Shape Contexts have been used to verify whether 2 shapes are similar or not. It has been used for various applications such as digit recognition, 3D Object recognition, trademark retrieval etc. In this paper we present a modified version of shape context for signature verification on bank cheques using K-Nearest Neighbor classifier.
An effective approach to offline arabic handwriting recognitionijaia
Segmentation is the most challenging part of the Arabic handwriting recognition, due to the unique
characteristics of Arabic writing that allows the same shape to denote different characters. In this paper,
an off-line Arabic handwriting recognition system is proposed. The processing details are presented in
three main stages. Firstly, the image is skeletonized to one pixel thin. Secondly, transfer each diagonally
connected foreground pixel to the closest horizontal or vertical line. Finally, these orthogonal lines are
coded as vectors of unique integer numbers; each vector represents one letter of the word. In order to
evaluate the proposed techniques, the system has been tested on the IFN/ENIT database, and the
experimental results show that our method is superior to those methods currently available.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Study on a Hybrid Segmentation Approach for Handwritten Numeral Strings in Fo...inventionjournals
This paper presents a hybrid approach to segment single- or multiple-touching handwritten numeral strings in form document, the core of which is the combined use of foreground, background and recognition analysis. The algorithm first located some feature points on both the foreground and background skeleton images containing connected numeral strings in form document. Possible segmentation paths were then constructed by matching these feature points, with an unexpected benefit of removing useless strokes. Subsequently, all these segmentation paths were validated and ranked by a recognition-based analysis, where a well-trained two-stage classifier was applied to each separated digit image to obtain its reliability. Finally, by introducing a locally optimal strategy to accelerate the recognition process, the top ranked segmentation path survived to help make a decision on whether to accept or not. Experimental results show that the proposed method can achieve a correct segmentation rate of 96.2 percent on a large dataset collected by our own.
DEVNAGARI DOCUMENT SEGMENTATION USING HISTOGRAM APPROACHijcseit
Document segmentation is one of the critical phases in machine recognition of any language. Correct
segmentation of individual symbols decides the accuracy of character recognition technique. It is used to
decompose image of a sequence of characters into sub images of individual symbols by segmenting lines and
words. Devnagari is the most popular script in India. It is used for writing Hindi, Marathi, Sanskrit and
Nepali languages. Moreover, Hindi is the third most popular language in the world. Devnagari documents
consist of vowels, consonants and various modifiers. Hence proper segmentation of Devnagari word is
challenging. A simple histogram based approach to segment Devnagari documents is proposed in this paper.
Various challenges in segmentation of Devnagari script are also discussed.
SCENE TEXT RECOGNITION IN MOBILE APPLICATION BY CHARACTER DESCRIPTOR AND STRU...Cheriyan K M
In text detection, our
previously proposed algorithms are applied to obtain text regions
from scene image. First, we design a discriminative character
descriptor by combining several state-of-the-art feature detectors
and descriptors. Second, we model character structure at each
character class by designing stroke configuration maps.
Hierarchical Approach for Total Variation Digital Image InpaintingIJCSEA Journal
The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consum ing process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.
Nowadays character recognition has gained lot of attention in the field of pattern recognition due to its application in various fields. It is one of the most successful applications of automatic pattern recognition. Research in OCR is popular for its application potential in banks, post offices, office automation etc. HCR is useful in cheque processing in banks; almost all kind of form processing systems, handwritten postal address resolution and many more. This paper presents a simple and efficient approach for the implementation of OCR and translation of scanned images of printed text into machine-encoded text. It makes use of different image analysis phases followed by image detection via pre-processing and post-processing. This paper also describes scanning the entire document (same as the segmentation in our case) and recognizing individual characters from image irrespective of their position, size and various font styles and it deals with recognition of the symbols from English language, which is internationally accepted.
An improved double coding local binary pattern algorithm for face recognitioneSAT Journals
Abstract A human face conveys a lot of information about the identity and emotional state of the person. So now a day’s face recognition has become an interesting and challenging problem. Face recognition plays a vital role in many applications such as authenticating a person, system security, verification and identification for law enforcement and personal identification among others. So our research work mainly consists of three parts, namely face representation, feature extraction and classification. The first part, Face representation represents how to model a face and check which algorithms can be used for detection and recognition purpose. In the second phase i.e. feature extraction phase we compute the unique features of the face image. In the classification phase the computed DLBP face image is compared with the images from the database. In our research work, we use Double Coding Local Binary Patterns to evaluate face recognition which concentrate over both the shape and texture information to represent face images for person independent face recognition. The face area is firstly cut into small regions from which Local Binary Patterns (LBP), then we compute histograms to generate LBP image then we compute single oriented mean image from which we again compute histogram values small regions and at last concatenated into a single feature vectors and generate D-LBP image. This feature are used for the representation of the face and to measure similarities between images. Keywords: local binary pattern (LBP), double coding local binary pattern (D-LBP), features extraction, classification, pattern recognition, histogram, feature vector.
Role Model of Graph Coloring Application in Labeled 2D Line Drawing ObjectWaqas Tariq
Several researches had worked on the development of sketch interpreters. However, very few of them gave a complete cycle of the sketch interpreter which can be used to transform an engineering sketch to a valid solid object. In this paper, a framework of the complete cycle of the sketch interpreter is presented. The discussion in this paper will stress on the usage of line labeling and graph coloring application in the validation of two dimensional (2D) line drawing phase. Both applications are needed to determine whether the given 2D line drawing represent possible or impossible object. In 2008, previous work by Matondang et al., has used line labeling algorithm to validate several 2D line drawings. However, the result shows that line labeling algorithm is not sufficient, as the algorithm does not have a validation technique for the result. Therefore, in this research study, it is going to be shown that if a 2D line drawing is valid as a possible object by using the line labeling algorithm, then it can be colored using graph coloring concept with a determine-able minimum numbers of color needed. This is equal in vice versa. The expected output from this phase is a valid-labeled of 2D line drawing with different colors at each edge and ready for the reconstruction phase. As a preliminary result, a high programming language MATLAB R2009a and several primitive 2D line drawings has been used and presented in this paper to test the graph coloring concept in labeled 2D line drawing.
Improvement of the Recognition Rate by Random ForestIJERA Editor
In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures
Improvement oh the recognition rate by random forestYoussef Rachidi
In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.
Performance analysis of chain code descriptor for hand shape classificationijcga
Feature Extraction is an important task for any Image processing application. The visual properties of any image are its shape, texture and colour. Out of these shape description plays important role in any image classification. The shape description method classified into two types, contour base and region based. The contour base method concentrated on the shape boundary line and the region based method considers whole area. In this paper, contour based, the chain code description method was experimented for different hand shape.
The chain code descriptor of various hand shapes was calculated and tested with different classifier such as k-nearest- neighbour (k-NN), Support vector machine (SVM) and Naive Bayes. Principal component analysis (PCA) was applied after the chain code description. The performance of SVM was found better than k-NN and Naive Bayes with recognition rate 93%.
TEXT EXTRACTION FROM RASTER MAPS USING COLOR SPACE QUANTIZATIONcsandit
Maps convey valuable information by relating names to their positions. In this paper we present
a new method for text extraction from raster maps using color space quantization. Previously,
most researches in this field were focused on Latin texts and the results for Persian or Arabic
texts were poor. In our proposed method we use a Mean-Shift algorithm with proper parameter
adjustment and consequently, we apply color transformation to make the maps ready for KMeans
algorithm which quantizes the colors in maps to six levels. By comparing to a threshold
the text layer candidates are then limited to three. The best layer can afterwards be chosen by
user. This method is independent of font size, direction and the color of the text and can find
both Latin and Persian/Arabic texts in maps. Experimental results show a significant
improvement in Persian text extraction.
A Comprehensive Study On Handwritten Character Recognition Systemiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
Cursive Handwriting Segmentation Using Ideal Distance Approach IJECEIAES
Offline cursive handwriting becomes a major challenge due to the huge amount of handwriting varieties such as slant handwriting, space between words, the size and direction of the letter, the style of writing the letter and handwriting with contour similarity on some letters. There are some steps for recursive handwriting recognition. The steps are preprocessing, morphology, segmentation, features of letter extraction and recognition. Segmentation is a crucial process in handwriting recognition since the success of segmentation step will determine the success level of recognition. This paper proposes a segmentation algorithm that segment recursive handwriting into letters. These letters will form words using a method that determine the intersection cutting point of image recursive handwriting with an ideal image distance. The ideal distance of recursive handwriting image is an ideal distance segmentation point in order to avoid the cutting of other letter’s section. The width and height of images are used to determine the accurate segmentation point. There were 999 recursive handwriting input images taken from 25 researchers used for this study. The images used are the images obtained from preprocessing step. Those are the images with slope correction. This study used Support Vector Machine (SVM) to recognize recursive handwriting. The experiments show the proposed segmentation algorithm able to segment the image precisely and have 97% success recognizing the recursive handwriting.
Comparative study of two methods for Handwritten Devanagari Numeral RecognitionIOSR Journals
Abstract : In this paper two different methods for Numeral Recognition are proposed and their results are
compared. The objective of this paper is to provide an efficient and reliable method for recognition of
handwritten numerals. First method employs Grid based feature extraction and recognition algorithm. In this
method the features of the image are extracted by using grid technique and this feature set is then compared
with the feature set of database image for classification. While second method contains Image Centroid Zone
and Zone Centroid Zone algorithms for feature extraction and the features are applied to Artificial Neural
Network for recognition of input image. Machine text recognition is important research area because of its
applications in many areas like Bank, Post office, Hospitals etc.
Keywords: Handwritten Numeral Recognition, Grid Technique, ANN, Feature Extraction, Classification.
Text detection and recognition in scene images or natural images has applications in computer
vision systems like registration number plate detection, automatic traffic sign detection, image retrieval
and help for visually impaired people. Scene text, however, has complicated background, blur image,
partly occluded text, variations in font-styles, image noise and ranging illumination. Hence scene text
recognition could be a difficult computer vision problem. In this paper connected component method
is used to extract the text from background. In this work, horizontal and vertical projection profiles,
geometric properties of text, image binirization and gap filling method are used to extract the text from
scene images. Then histogram based threshold is applied to separate text background of the images.
Finally text is extracted from images.
A Novel Framework For Numerical Character Recognition With Zoning Distance Fe...IJERD Editor
Advancements of Computer technology has made every organization to implement the automatic processing systems for its activities. One of the examples is the recognition of handwritten characters, which has always been a challenging task in image processing and pattern recognition. In this paper we propose Zone based features for recognition of the handwritten characters. In this zoning approach a digit image is divided into 8x8 zones and centre pixel is computed for each zone. This procedure is sequentially repeated for entire zone. Finally features are extracted for classification and recognition.
Two Methods for Recognition of Hand Written Farsi CharactersCSCJournals
Optical character recognition (OCR) is one of the active bases of sample detection topics. The current study focuses on automatic detection and recognition of hand written Farsi characters. For this purpose; we proposed two different methods based on neural networks and a special post processing approach to improve recognition rate of Farsi uppercase letters. In the first method, we extracted wavelet features from borders of character images and learned a neural network based these patterns. In the second method, we divided input characters into five groups according to the number of their components and used a set of appropriate moment features in each group and classified characters by the Bayesian rule. In a post-processing stage, some structural and statistical features were employed by a decision tree classifier to reduce the misrecognition rate. Our experimental results show suitable recognition rate for both methods.
Similar to Classifier fusion method to recognize (20)
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Classifier fusion method to recognize
1. International Journal on Cybernetics & Informatics (IJCI) Vol. 3, No. 3, June 2014
DOI: 10.5121/ijci.2014.3301 1
CLASSIFIER FUSION METHOD TO RECOGNIZE
HANDWRITTEN PERSIAN NUMERALS
Reza Azad1
, Babak Azad2
, Iraj Mogharreb3
, Shahram Jamali4
1
Department of Electrical and Computer Engineering, Shahid Rajaee Teacher Training
University, Tehran, Iran
2
Computer Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran
3
Ardabil Branch Islamic Azad University, Ardebil, Iran
4
Associate professor, Faculty of Computer Engineering, University of Mohaghegh
Ardabili, Ardabil, Iran
ABSTRACT
Recognition of Persian handwritten characters has been considered as a significant field of research for
the last few years under pattern analysing technique. In this paper, a new approach for robust handwritten
Persian numerals recognition using strong feature set and a classifier fusion method is scrutinized to
increase the recognition percentage. For implementing the classifier fusion technique, we have considered
k nearest neighbour (KNN), linear classifier (LC) and support vector machine (SVM) classifiers. The
innovation of this tactic is to attain better precision with few features using classifier fusion method. For
evaluation of the proposed method we considered a Persian numerals database with 20,000 handwritten
samples. Spending 15,000 samples for training stage, we verified our technique on other 5,000 samples,
and the correct recognition ratio achievedapproximately 99.90%. Additional, we got 99.97% exactness
using four-fold cross validation procedure on 20,000 databases.
KEYWORDS
Persian handwritten recognition, k nearest neighbor, linear classifier, SVM classifier, classifier fusion.
1. INTRODUCTION
Nowadays handwritten characters recognition is one of the most popular research areas, because
it has various application potentials. Bank cheques processing, Postal Automation, Automatic
data entry, etc. are some of its potential application are. Most of the handwritten character
recognition methods for, Arabic, English, and Chinese scripts are reviewed in [1-3]. As regards
there is no popular method for Persian handwritten character recognition due to cursive-ness of
Persian handwritten, and various way of characters combination together and also, characters
position in words. By the following passages, we studied edge maps, transit and directional
frequencies effect in the numeral image contour pixels as features, which kept morphological
information of input and then applied fusion of classifiers as classifier.
In Iran and some of its neighbouring countries, the Persian numerals have usage. Also, the
Persian has 10 numerals. Alphabets of Persian and Arabic scripts are written from right to left but
in their text, digits are taken place from left to right. Despite the similarity of Persian and Arabic
numerals, there are a few important differences between their scripts [4]. Normally, in Persian
2. International Journal on Cybernetics & Informatics (IJCI) Vol. 3, No. 3, June 2014
2
there are two types of writing for the digits 0, 2, 4, 5 and 6. These characteristics make Persian
numerals recognition so sophisticate than other languages [4]. Fig. 1 shows the Examples of
Persian printed and handwritten digits.
Figure 1. Sample of Persian Handwritten numerals [4]
In the related workspertinent to the Persian handwritten character recognition, many approaches
for feature extraction and classificationhas been proposed. Some of the latest methods in field of
feature extraction are; shadow and segmentation codes [5-7], fractal approach [8], profiles [9],
moment features [10], template matching [11], structural feature set [12] and wavelet [13], [14].
Also for classification stage different types of Neural Networks [5-8], [10], [11], SVM’s [9], [13],
[15], Nearest Neighbour [12], multiple classifier [16], [17] have been implemented. What
achieved from the literature survey on Persian handwritten character recognition, it is clear that
not much attempt was increased to recognize a more capable feature set (some of them are time
consuming and some of them cannot keep the structure of the input image for feature extraction
stage), which could more suitably be respond to the recognition part [4]. For solving this kind of
issue, we investigatedmore robust featureswith use of transit, edge maps and modified contour
chain code of every window-map, and then apply fusion of classifiers for classification. This kind
of feature set, which explicit the somatic shape of input character and take out local information
of the input image in each window-map, provided very good correctness in experimental stage.
We ought toremarkthat the suggested system did not use any pre-processing methods (skew and
tilt detection/improvement, smooth out, noise elimination, etc.) that were luxuriousprocesses.
Likewise, strength of our feature set was under treatment some of these issues such as skew and
slant reasonably.
The rest of our paper is organized as follows: feature extraction technique is detailed in Section
two, classification stage id described in Section three, experimental results and comparative
investigation are labelled in Section four and finally in last section we assign the conclusion.
2. FEATURE EXTRACTION TECHNIQUE
In this phase we will extract chain-code, modified edge maps and transition feature set. Extracting
tactic of these feature set are detailed in the next subsections.
2.1. Directional Chain Code FrequenciesFeature Set
Directional chain code frequencies of the outline points of the input image are very useful for
different application such as character segmentation, recognition, etc. [18]. In our proposed
method as a first feature set we extracted chain-code directional frequencies of outline pixels of
the images by the following rule: First the minimum rectangle covering the handwritten character
(bounding box) is extracted for every input image, Then for removing the features to size and
3. International Journal on Cybernetics & Informatics (IJCI) Vol. 3, No. 3, June 2014
3
position, we adapted each image to a normal size of 49×49 pixels. We selected this normalized
value based of manyexperimentations and a geometric study. Fig. 2(a) shows the normalized
image with its covering rectangle and in Fig. 2(b) the extracted outline points of the character is
shown.
Figure 2. (a): bordering of a normalized image (b): Digit ‘5’ outlineshape
By possession a window-map of size 7×7 on the image, we scanned the image outline
horizontally from the top left maximum point to flatmaximum point (that contains 49 no
overlapped blocks) and we extracted a8 directions chain code frequencies for each block (8
directions were depicted in Fig. 3(a)). As a replacementextracting the feature set in terms of 8
orders, we have offered to simplify the features into 4 sets fit to 4 orders (Fig. 3(b) shows the four
directions), that the horizontal direction code are determined by the 0 and 4 directions, vertical
direction code are showed bythe direction 2 and 6, also the principal diagonal direction are
determined by the direction 1 and 5 and finallythe off diagonal direction code is masked by the 3
and 7 directions. Therefore, in every block, we acquired four values signifying the occurrences of
these four ordersand these quantities were used as local contour direction featureset.For extracting
these features, an unvarying block with 49 (7×7) size is considered in every image and we
calculated four features (four directions) in each block so we acquired 49×4=196 features for
every image.
Figure 3. (a): chain code pattern for 8 direction (b): chain code pattern for 4 direction
2.2. Modified Edge Maps Features
In this stage, at the beginning, an N x M image is converted into the thinning form and then
reshaped into a 49 × 49 matrix. For extracting the four distinct edge maps (horizontal, vertical
and two diagonals (45° and -45°)) the Sobel operators was used. After that these four distinct
edge maps are distributed into 49 sub-images of 7 × 7 pixels.Then the featureset are
gottencomputing the proportion of black pixels in each sub-image (a featureset with 49 dimension
for each image). Finally these features are joint to form a single feature vector holding 196 (49 x
4) features.
2.3. Transition features
The third feature set that investigated in this paperis based on the extracting the transition value
from background to foreground pixels in both vertical and horizontal directions. Our extracting
The transition feature set is mostly like a transition feature set that hass been proposed by Kumar
et al [19]. forextracting transition information, image is skimmed from top to bottom and left to
right. Following actions shows the way of these feature extraction.
4. International Journal on Cybernetics & Informatics (IJCI) Vol. 3, No. 3, June 2014
4
Action1: Distribute the contoured image of a handwritten image into 49 part with size of 7×7.
Action2: Computethe number of transitions for each part and extract the 49 features for each
character image.
3. CLASSIFICATION BY ENSEMBLE TECHNIQUE
Ensemble technique has wonderful application in different techniques and widely used in pattern
classification and machine learning. the diversekinds of ensemble techniques has been proposed
before and among them theclassification fusion method is the most important type of the
ensemble classification. In this technique, numerous classifiers are trained on a same feature
space and then,the results of these classifiers are mixed to get a more precise classification [20].
In the current paper, we have used K-Nearest Neighbour (KNN), Linear (L) and Support Vector
Machine (SVM) classifiers for ensemble technique. The features gained from Directional Chain
Code (DCD) are applied to SVM, the Modified Edge Maps (MEM) feature Set is applied to linear
classifiers and Transition features are applied to KNN separately. The prediction of these
classifiers is combined using majority-voting procedure to appropriately classify the sample.
3.1. Classification with use of the K-Nearest Neighbour
If you faced with the classification problem has pattern classes you can use an efficient technique
called the K-nearest neighbor classifier that display a reasonably limited degree of variability.
With calculating the distance between the input pattern and the training patterns it could
recognize each input pattern with certain accuracy that has given.During the classification only k
nearest prototypes could takes into the input pattern.the final decision is performed with use of
majority of class voting.In the k-Nearest neighbour technique, the distance among train and test
set is computed for determining the class of the test set.In the Equation (1) the applying method is
detailed:
݀ = ට (ݔ − ݕ)ଶ
ୀଵ
(1)
In the above equation,xk is the collection stored feature value, N shows the entire number of
features in feature set and yk is the nominee feature value.
3.2. Linear Classifier (LC)
Linear classifier (LC) is a kind of the statistical classifier thatuses a value of the linear mixture of
the featuresfor generating alabel of class.The application of the linear classifier is mostly on the
circumstances where the speed of classification is an important
issue.LCfrequentlyeffortexcellently when the number of magnitudes in feature vector is huge. It
can be signified as revealed in equation (2):
y= f(w, x) = f൫∑ w୨x୨୨ ൯ (2)
Which w୨ is weight vector, learned from a set of marked training examples and thex୨ is the
feature vector of testing model and f is a simple function that applies the value to the individual
classes based on a confident threshold.
5. International Journal on Cybernetics & Informatics (IJCI) Vol. 3, No. 3, June 2014
5
3.3. Support Vector Machines (SVM) Classifier
Support vector machines (SVMs) are one of the most importont, and powerfull in pattern learning
and also in pattern recognition, because it support high dimensional data and at the same time,
providing good generalization properties. Additionally, in data mining and pattern recognition
applications, SVMs have very usages. Considered that ܺ = ሼ(ݔ, ݕ)ሽ
݅ = 1 are samples of n
training, where, ݕ ∈ ሼ−1,1ሽ is a class label of sample xi, and ݔ ∈ ܴ
is an m-dimensional
sample in the input space. with the minimal classification errors, SVM finds the optimal
separating hyper plane (OSH). Equation (3) shown the linear separation hyper plane.
݂()ݔ = ்ܹ
ݔ + ܾ (3)
Here b and W are the bias, and weight vector. By solving the optimization problem (6), the
optimal hyper plane can be obtain, where variable C controls the effect of the slack variables, and
ߞis slack variable for obtaining a soft margin. Decreasing the value of C cause to increasing of
separation margin. In a SVM, maximizing the generalization ability of the SVM cause to optimal
hyper plane obtained. Anyhow, the obtained classifier may not have high generalization ability in
a nonlinear separable training data. The original input space should be mapped into a high-
dimensional in order to enhancing the linear severability purpose. Now, with using the nonlinear
vector function߮()ݔ = (߮ଵ(,)ݔ … , ߮())ݔஏ
, witch maps the m-dimensional input vector x into
the l-dimensional feature space, the OSH in the feature space is given by equation (4):
f(x) = W
φ(x) + b (4)
Equation (5) defined a decision function for a test data:
D(x) = Sign(W
φ(x) + b) (5)
By solving the following quadratic optimization problem, the optimal hyper plane can be found:
Minimize
1
2
||W||ଶ
+ C ζ୧
୬
୧ୀଵ
Subject to y୧(W
φ(x) + b) ≥ 1 − ζ୧
ζ୧ ≥ 0, i = 1, … , n (6)
As mentioned before, SVMs classifier isintroducedfor binary problemclassification, yet, our
proposed method has more than two classes for classifications. For solving this problem,
multiclass classification strategies that mentioned in [21] can be used. The greatestcommon ones
are the one-against-one (OAO) and the one-against-all (OAA) approaches [22]. The one-against-
one isusing the (n (n-1)) ⁄2 equation for combinations of all class pairs. What achieved from the
experimental results we find out that the one-against-all is more fitting for our proposed method.
We used OAA for our character classification.
Our proposed classifier fusion method is sum up in the below algorithm. In addition Fig. 4 shows
the recognition way for entrance test image by applying fusion of classifier.
6. International Journal on Cybernetics & Informatics (IJCI) Vol. 3, No. 3, June 2014
6
Algorithm Begin:
Input: a set of test samples and training samples.
Output: the class label for determining the test sample belongs.
Method: Step1: Extract the directional chain code (DCD), Modified Edge Maps (MEM) and
transit (T) features for the training sample and test sample using previously discussed approaches
respectively.
Step 2: Apply the DCD, MEM and T features obtained for the training samples to train the SVM,
KNN and L classifiers respectively and separately.
Step 3: Apply the DCD, MEM and T features obtained for the test samples to each of the
classifier.
Let the prediction of the classifiers be p1, p2 and p3
Step 4: Predict the class of the test sample as
Class = Majority of the {p1, p2, p3}
End
Figure 4. Architecture of the classifier fusion method to recognizehandwritten numerals
4. PRACTICAL RESULTS OF THE PROPOSED METHOD
For analysing of the proposed method, a set of the 15,000 samples for training stage and a set of
the 5,000 for test stage are considered as indicated in [23]. These samples contain Iranian Postal
and National Codes and were extracted with use of the 200 dpi resolution scanner from different
registration forms of the Iranian university entrance exam [24]. As we mentioned in section 2 the
writing styles of different persons, samples sizes were extremely different, we standardized them
to the constant size. By considering the 15,000 samples for the training stage, we evaluated our
method on other 5,000 samples and we achieved 99.90% precision. What achieved from the
experimental result, we obtained an exactness of 100% when the 20,000 data were utilized as
Handwritten Character Image
Extract DCD
Feature Set
Extract MEM
Feature Set
Extract T
Feature Set
0
Apply SVM
Classifier
Apply L
Classifier
Apply KNN
Classifier
0
Majority of
Vote
Recognized
Character
7. International Journal on Cybernetics & Informatics (IJCI) Vol. 3, No. 3, June 2014
7
training stage and the similar dataset was used for testing stage. For further analysing we
separated our data set into 4 subclass and testing is estimated on each subclass using reminder of
the 3 subsets for training stage. The mean of the recognition rates for all the four test subclass is
achieved about 99.97%. Table 1 shows the performance comparison of the proposed method with
the state of the art methods.
Table 1. Recognition rates of the diverseapproachesfor recognizing the Persian handwritten numerals
Method
Database size Accuracy of the method (%)
Train Test Train Test
Azad et al. [4] 15000 5000 - 99.82
Shahreza et al.[5] 2600 1300 - 97.80
Harifi et al. [6] 230 500 - 97.60
Hosseini et al. [7] 480 480 - 92.00
Mozaffari et al. [8] 2240 1600 98 91.37
Rahmati et al.[9] 4979 3939 - 99.57
Dehghan et al. [10] 6000 4000 - 97.01
Faes et al. [11] 6000 4000 100 97.65
Faes et al. [12] 2240 1600 100 94.44
Mowlaei et al. [13] 2240 1600 100 92.44
Mowlaei et al. [14] 2240 1600 99.29 91.88
Sadri et al. [15] 7390 3035 - 94.14
Parvin, et al [16] 40000 2000 - 97.12
Parvin, et al [17] 60000 10000 - 98.89
Our proposed method 15000 50000 100 99.90
Our proposed method
With 4 subset
15000 50000 100 99.97
For evaluation of the proposed method we used a dataset with 20,000 samples and weachieved
99.90% and 99.97% precisions using mentioned method. In our achievement with high accuracy
(about 99.90%), we detected confusion numerals in the recognition stageamong some digits. The
main confusions were between 2, 4 and 3. This occurredsince 2, 3 and 4 resemble each other. In
Fig. 5 the success and confusion rate for each character are depicted.
8. International Journal on Cybernetics & Informatics (IJCI) Vol. 3, No. 3, June 2014
Figure 5. Success and confusion rate of the proposed
5. CONCLUSIONS
In this paper, for robust Persian handwritten numerals recognition
feature extraction approachessuch as
obtaining these feature set we transformed
block based method we achieved
nearest neighbour classifiers are used for the classification. Further, the re
enhancedwith use of classifier synthesis
could be proofed that oursproposed techniquehas
numeral recognition. In addition
misclassification.Most of the misclassification
and 4, which have similar structure.
ACKNOWLEDGEMENTS
This research is supported by the S
(No.22970060-9).
REFERENCES
[1] A. Yuan, G. Bai, L. Jiao, Y. Liu, “Offline handwritten English character recognition based on
convolutional neural network “, 10th IEEE International Workshop on Document Analysis Systems,
2012, pp. 125-129.
[2] S. N. Srihari and G. Ball, "An Assessment of Arabic Handwriting Recognition Technology", Springer
book of Guide to OCR for Arabic Scripts, 2012, pp. 3
[3] F. Yin, M. Zhou, Q. Wang and C. Liu, “ Style Consistent Perturbation for Handwri
Character Recognition “, 12th IEEE International Conference onDocument Analysis and Recognition,
2013, pp. 1051-1055.
90%
91%
92%
93%
94%
95%
96%
97%
98%
99%
100%
0 1
Confusion Rate 0.05 0.03
Success Rate 99.95 99.97
International Journal on Cybernetics & Informatics (IJCI) Vol. 3, No. 3, June 2014
and confusion rate of the proposed approachfor each Persian numeral
Persian handwritten numerals recognition we have investigated
approachessuch as directional chain code, modified edge maps and transit
transformed each image to the contour shape, then
achieved these three features sets. In the beginning, SVM, Linear and K
nearest neighbour classifiers are used for the classification. Further, the recognition accuracy was
synthesis method. What achieved from the experimental results, it
proposed techniquehas good performances on Persian handwritten
numeral recognition. In addition, in the result part we detailedthe reason of the
Most of the misclassification samples on our method were from classes of 2, 3
structure.
is supported by the Shahid Rajaee Teacher Training University, Tehr
A. Yuan, G. Bai, L. Jiao, Y. Liu, “Offline handwritten English character recognition based on
convolutional neural network “, 10th IEEE International Workshop on Document Analysis Systems,
S. N. Srihari and G. Ball, "An Assessment of Arabic Handwriting Recognition Technology", Springer
book of Guide to OCR for Arabic Scripts, 2012, pp. 3-34.
F. Yin, M. Zhou, Q. Wang and C. Liu, “ Style Consistent Perturbation for Handwri
Character Recognition “, 12th IEEE International Conference onDocument Analysis and Recognition,
2 3 4 5 6 7 8
0.44 0.31 0.4 0.11 0.01 0 0.01
99.56 99.69 99.6 99.89 99.99 100 99.99 100
International Journal on Cybernetics & Informatics (IJCI) Vol. 3, No. 3, June 2014
8
Persian numeral
investigated three
directional chain code, modified edge maps and transit. For
then with use of
, SVM, Linear and K
cognition accuracy was
experimental results, it
on Persian handwritten
the reason of the
were from classes of 2, 3
University, Tehran, Iran
A. Yuan, G. Bai, L. Jiao, Y. Liu, “Offline handwritten English character recognition based on
convolutional neural network “, 10th IEEE International Workshop on Document Analysis Systems,
S. N. Srihari and G. Ball, "An Assessment of Arabic Handwriting Recognition Technology", Springer
F. Yin, M. Zhou, Q. Wang and C. Liu, “ Style Consistent Perturbation for Handwritten Chinese
Character Recognition “, 12th IEEE International Conference onDocument Analysis and Recognition,
9
0
100
9. International Journal on Cybernetics & Informatics (IJCI) Vol. 3, No. 3, June 2014
9
[4] R. Azad, F. Davami and H. Shayegh, “Recognition of Handwritten Persian/Arabic Numerals Based
on Robust Feature Set and K-NN Classifier”, International Journal of Computer & Information
Technologies, Vol. 1, issue 3, 2013, pp. 220-230.
[5] M. H. Shirali-Shahreza, K. Faez and A. Khotanzad, “Recognition of Hand-written Persian/Arabic
Numerals by Shadow Coding and an Edited Probabilistic Neural Network“, IEEE International
Conference on Image Processing, Vol. 3, 1995, pp. 436-439.
[6] A. Harifi and A. Aghagolzadeh, ”A New Pattern for Handwritten Persian/Arabic Digit Recognition”,
Journal of Information Technology Vol. 3, 2004, pp. 249-252.
[7] H. Mir Mohammad Hosseini and A. Bouzerdoum, ”A Combined Method for Persian and Arabic
Handwritten Digit Recognition”, Australian New Zealand Conference on Intelligent Information
System, 1996, pp. 80 – 83.
[8] S. Mozaffari, K. Faez& H. RashidyKanan, “Recognition of Isolated Handwritten Farsi/Arabic
Alphanumeric Using Fractal Codes”, Image Analysis and Interpretation, 6th Southwest Symposium,
2004, pp. 104-108.
[9] H. Soltanzadeh and M. Rahmati, “Recognition of Persian handwritten digits using image profiles of
multiple orientations”, Pattern Recognition Letters 25, 2004, pp. 1569–1576.
[10] M. Dehghan and K. Faez, “Farsi Handwritten Character Recognition With Moment Invariants”,
Proceedings of 13th International Conference on Digital Signal Processing, Volume 2, 1997, pp. 507-
510.
[11] M. Ziaratban, K. Faez and F. Faradji, “Language-Based Feature Extraction Using Template-Matching
in Farsi/Arabic Handwritten Numeral Recognition”, Proceedings of 9th International Conference on
Document Analysis and Recognition, Vol.1, 2007, pp. 297-301.
[12] S. Mozaffari, K. Faez and M. Ziaratban, “Structural Decomposition and Statistical Description of
Farsi/Arabic Handwritten Numeric Characters”, Proceedings of the 8th Intl. Conference on Document
Analysis and Recognition, Vol. 1, 2005, pp. 237- 241.
[13] A. Mowlaei and K. Faez, “Recognition Of Isolated Handwritten Persiawarabic Characters And
Numerals Using Support Vector Machines”, Proceedings of XIII Workshop on Neural Networks for
Signal Processing, 2003, pp. 547-554.
[14] A. Mowlaei, K. Faez& A. Haghighat, ”Feature Extraction with Wavelet Transform for Recognition of
Isolated Handwritten Farsi/Arabic Characters and Numerals”, Digital Signal Processing Vol. 2, 2002,
pp. 923- 926.
[15] J. Sadri, C. Y. Suen and T. D. Bui, “Application of Support Vector Machines for Recognition of
Handwritten Arabic/Persian Digits”, Proceedings of the 2nd Conference on Machine Vision and
Image Processing & Applications, Vol. 1, 2003, pp. 300-307.
[16] H. Parvin, H. Alizadeh, M. Moshki, B. Bidgoli and N. Mozayani, ” Divide & Conquer Classification
and Optimization by Genetic Algorithm”, Third 2008 International Conference on Convergence and
Hybrid Information Technology, 2008, pp. 858 – 863.
[17] H. Parvin, H. Alizadeh, B. Bidgoli and M. Analoui, ” A Scalable Method for Improving the
Performance of Classifiers in Multiclass Applications by Pairwise Classifiers and GA”, Fourth
International Conference on Networked Computing and Advanced Information Management , 2008,
pp. 137 –142.
[18] R. Azad, B. Azad, "Real-Time Hand Gesture Recognition based on Modified Contour Chain Code
Feature Set", IJIGSP, vol.6, no.8, pp.25-31, 2014.DOI: 10.5815/ijigsp.2014.08.04.
[19] M. Kumar, M. K. Jindal and R. K. Sharma, "k -Nearest Neighbor Based Offline Handwritten
Gurmukhi Character Recognition," IEEE International Conference on Image Information Processing,
pp.1-4, 2013.
[20] D. Parikh and R. Polikar, “An Ensemble-Based Incremental Learning Approach to Data Fusion”,
IEEE Transaction on System, Man and Cybernetics, vol.37, no.2. April,2007 pp.437-450.
[21] F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support
vector machine,” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, 2004, pp. 1778–1790.
[22] R. Azad, B. Azad and I. T. Kazeroni , “Optimized Method for Real-Time Face Recognition System
Based on PCA and Multiclass Support Vector Machine”, Advances in Computer Science: an
International Journal, Vol. 2, Issue 5, No.6 , November 2013, pp. 126-132.
10. International Journal on Cybernetics & Informatics (IJCI) Vol. 3, No. 3, June 2014
10
[23] R. Azad, F. Davami and H. Shayegh, “Recognition of Handwritten Persian/Arabic Numerals Based
on Robust Feature Set and K-NN Classifier”, International Conference on computer, Information
Technology and Digital Media, 2013, pp. 161-165.
[24]H. Khosravi and E. Kabir, ”Introducing a very large dataset of handwritten Farsi digits and a study on
the variety of handwriting styles”, Pattern Recognition Letters, 28(10), 2007, pp. 1133-1141.
Authors
Reza Azadobtained his B.Sc. degree with honor in computer software engineering from
SRTTU in 2014. He is IEEE & IEEE conference reviewer Member. Awarded as best
student in 2013 and 2014 by the SRTTU and awarded as best researcher in 2013 by the
SRTTU. He achieved fourth place in Iranian university entering exam. In addition he’s a
member of Iranian elites. He has a lot of scientific papers in international journal and
conferences, such as IEEE, Springer and etc. his interested research are artificial intelligence and computer
vision.
Babak Azadis a researcher from Islamic Azad University. He achieved a lot of awards
and publication on scientific papers in international journals and conferences, during his
B.Sc. education. His most interest topics are machine learning and network.
Iraj Mogharreb is a B.Sc. student in Sabalan Higher educatin institute, Ardebil, Iran, in
computer software engineering and he is top student in university. His research interests
include image processing, machine learning and information security.
ShahramJamaliis currently an Associate Professor in Mohaghegh Ardabili University,
Ardebil, Iran. He achieved his Ph.D degree in Architecture of Computer Systems in
2008 from Iran University of Science & Technology, Tehran, Iran. He has more than
100 scientific papers in international journals and conferences, such as IEEE, Elsevier,
Springer and etc. His research topics are Network security and soft computing.