Sign language is the key for communication between deaf people. The significance of sign language is accentuated by various research activities and the technical aspects will definitely improve the communication needs. General view based sign language recognition systems extract manual parameters by a single camera view because it seems to be user friendly and hardware complexity; however it needs a high accuracy classifier for classification and recognition purpose. The decision making of the system in this work employs Indian sign language datasets and the performance evaluation of the system is compared by deploying the K-NN, Naïve Bayes and PNN classifiers. Classification using an instance-based classifiercan be a simple matter of locating the instance space and labelling the unknown instance with the same class label as that of the located (known) neighbour. Classifier always tries to improve the classification rate by pushing classifiers into an optimised structure. In each hand posture, a measure of properties like area, mean intensity, centroid, perimeter and diameter are taken; the classifier then uses these properties to determine the sign in different angles. They estimate the probability that a sign belongs to each of the target classes that is fixed. The impact of such study may reflect the exploration for using such algorithms
in other similar applications such as text classification and the development of automated systems.
LITERATURE SURVEY ON SPARSE REPRESENTATION FOR NEURAL NETWORK BASED FACE DETE...csijjournal
Face detection and recognition is a challenging problem in the field of image processing. In this paper, we reviewed some of the recent research works on face recognition. Issues with the previous face recognition
techniques are , time required is more for face recognition , recognition rate and database required to store the data . To overcome these problems sparse representation based classifier technique can be used.
Parameters Optimization for Improving ASR Performance in Adverse Real World N...Waqas Tariq
From the existing research it has been observed that many techniques and methodologies are available for performing every step of Automatic Speech Recognition (ASR) system, but the performance (Minimization of Word Error Recognition-WER and Maximization of Word Accuracy Rate- WAR) of the methodology is not dependent on the only technique applied in that method. The research work indicates that, performance mainly depends on the category of the noise, the level of the noise and the variable size of the window, frame, frame overlap etc is considered in the existing methods. The main aim of the work presented in this paper is to use variable size of parameters like window size, frame size and frame overlap percentage to observe the performance of algorithms for various categories of noise with different levels and also train the system for all size of parameters and category of real world noisy environment to improve the performance of the speech recognition system. This paper presents the results of Signal-to-Noise Ratio (SNR) and Accuracy test by applying variable size of parameters. It is observed that, it is really very hard to evaluate test results and decide parameter size for ASR performance improvement for its resultant optimization. Hence, this study further suggests the feasible and optimum parameter size using Fuzzy Inference System (FIS) for enhancing resultant accuracy in adverse real world noisy environmental conditions. This work will be helpful to give discriminative training of ubiquitous ASR system for better Human Computer Interaction (HCI). Keywords: ASR Performance, ASR Parameters Optimization, Multi-Environmental Training, Fuzzy Inference System for ASR, ubiquitous ASR system, Human Computer Interaction (HCI)
This paper introduces a new concept for the establishment of human-robot symbiotic relationship. The
system is based on the implementation of knowledge-based image processing methodologies for model
based vision and intelligent task scheduling for an autonomous social robot. This paper aims to develop an
automatic translation of static gestures of alphabets and signs in American Sign Language (ASL), using
neural network with backpropagation algorithm. System deals with images of bare hands to achieve the
recognition task. For each individual sign 10 sample images have been considered, which means in
total300 samples have been processed. In order to compare between the training set of signs and the
considered sample images, are converted into feature vectors. Experimental results reveal that this can
recognize selected ASL signs (accuracy of 92.00%). Finally, the system has been implemented issuing hand
gesture commands for ASL to a robot car, named “Moto-robo”.
An exhaustive font and size invariant classification scheme for ocr of devana...ijnlc
Main challenge in any Optical Character Recognition (OCR) system is to deal with multiple fonts and sizes. In OCR of Indian languages, one also has to deal with a huge number of conjunct characters whose shape changes drastically with fonts. Separating the conjunct characters into its constituent symbols leads to segmentation errors. The proposed approach handles both the above listed problems in the context of Devanagari script. An attempt is made to identify all possible connected symbols of Devanagari (could be a consonant, vowel, half consonant or conjunct consonant henceforward shall be referred as a basic symbol) in the middle zone without segmenting the conjunct characters. On observing 469580 words from a variety of sources in our study, it is found that only 345 symbols are used more frequently in the middle zone and cover 99.97% of the text. They are then classified into 16 different classes on the basis of structural properties which are invariant across fonts and sizes. To validate the proposed classification scheme, results are presented on 25 fonts and three sizes.
The presentation will describe an algorithm through which one can recognize Devanagari Characters. Devanagari is the script in which Hindi is represented. This algorithm
could automatically segment character from the image of Devenagari text and then recognize them.
For extracting the individual characters from the image of Devanagari text, algorithm segmented the image several
times using the vertical and horizontal projection.
The algorithm starts with first segmenting the lines separately from the document by taking horizontal projection and then the line
into words by taking vertical projection of the line. Another step which is particular to the separation of
Devanagari characters was required and was done by first removing the header line by finding horizontal projection
of each word. The characters can then be extracted by vertical projection of the word without the header line.
Algorithm uses a Kohonen Neural Netowrk for the recognition task. After the separation of the characters from the
image, the image matrix was then downsampled to bring it down to a fixed size so as to make the recognition
size independent. The matrix can then be fed as input neurons to the Kohonen Neural Network and the winning neuron is
found which identifies the recognized the character. This information in Kohonen Neural Network was stored
earlier during the training phase of the neural network. For this, we first assigned random weights from input neurons
to output neurons and then for each training set, the winning neuron was calculated by finding the maximum
output produced by the neurons. The wights for this winning neuron were then adjusted so that it responds to this
pattern more strongly the next time.
Vocabulary length experiments for binary image classification using bov approachsipij
Bag-of-Visual-words (BoV) approach to image classification is popular among computer vision scientists.
The visual words come from the visual vocabulary which is constructed using the key points extracted from
the image database. Unlike the natural language, the length of such vocabulary for image classification is
task dependent. The visual words capture the local invariant features of the image. The region of image
over which a visual word is constrained forms the spatial content for the visual word. Spatial pyramid
representation of images is an approach to handle spatial information. In this paper, we study the role of
vocabulary lengths for the levels of a simple two level spatial pyramid to perform binary classifications.
Two binary classification problems namely to detect the presence of persons and cars are studied. Relevant
images from PASCAL dataset are being used for the learning activities involved in this work
An Improved Approach for Word Ambiguity RemovalWaqas Tariq
Word ambiguity removal is a task of removing ambiguity from a word, i.e. correct sense of word is identified from ambiguous sentences. This paper describes a model that uses Part of Speech tagger and three categories for word sense disambiguation (WSD). Human Computer Interaction is very needful to improve interactions between users and computers. For this, the Supervised and Unsupervised methods are combined. The WSD algorithm is used to find the efficient and accurate sense of a word based on domain information. The accuracy of this work is evaluated with the aim of finding best suitable domain of word. Keywords: Human Computer Interaction, Supervised Training, Unsupervised Learning, Word Ambiguity, Word sense disambiguation
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.
LITERATURE SURVEY ON SPARSE REPRESENTATION FOR NEURAL NETWORK BASED FACE DETE...csijjournal
Face detection and recognition is a challenging problem in the field of image processing. In this paper, we reviewed some of the recent research works on face recognition. Issues with the previous face recognition
techniques are , time required is more for face recognition , recognition rate and database required to store the data . To overcome these problems sparse representation based classifier technique can be used.
Parameters Optimization for Improving ASR Performance in Adverse Real World N...Waqas Tariq
From the existing research it has been observed that many techniques and methodologies are available for performing every step of Automatic Speech Recognition (ASR) system, but the performance (Minimization of Word Error Recognition-WER and Maximization of Word Accuracy Rate- WAR) of the methodology is not dependent on the only technique applied in that method. The research work indicates that, performance mainly depends on the category of the noise, the level of the noise and the variable size of the window, frame, frame overlap etc is considered in the existing methods. The main aim of the work presented in this paper is to use variable size of parameters like window size, frame size and frame overlap percentage to observe the performance of algorithms for various categories of noise with different levels and also train the system for all size of parameters and category of real world noisy environment to improve the performance of the speech recognition system. This paper presents the results of Signal-to-Noise Ratio (SNR) and Accuracy test by applying variable size of parameters. It is observed that, it is really very hard to evaluate test results and decide parameter size for ASR performance improvement for its resultant optimization. Hence, this study further suggests the feasible and optimum parameter size using Fuzzy Inference System (FIS) for enhancing resultant accuracy in adverse real world noisy environmental conditions. This work will be helpful to give discriminative training of ubiquitous ASR system for better Human Computer Interaction (HCI). Keywords: ASR Performance, ASR Parameters Optimization, Multi-Environmental Training, Fuzzy Inference System for ASR, ubiquitous ASR system, Human Computer Interaction (HCI)
This paper introduces a new concept for the establishment of human-robot symbiotic relationship. The
system is based on the implementation of knowledge-based image processing methodologies for model
based vision and intelligent task scheduling for an autonomous social robot. This paper aims to develop an
automatic translation of static gestures of alphabets and signs in American Sign Language (ASL), using
neural network with backpropagation algorithm. System deals with images of bare hands to achieve the
recognition task. For each individual sign 10 sample images have been considered, which means in
total300 samples have been processed. In order to compare between the training set of signs and the
considered sample images, are converted into feature vectors. Experimental results reveal that this can
recognize selected ASL signs (accuracy of 92.00%). Finally, the system has been implemented issuing hand
gesture commands for ASL to a robot car, named “Moto-robo”.
An exhaustive font and size invariant classification scheme for ocr of devana...ijnlc
Main challenge in any Optical Character Recognition (OCR) system is to deal with multiple fonts and sizes. In OCR of Indian languages, one also has to deal with a huge number of conjunct characters whose shape changes drastically with fonts. Separating the conjunct characters into its constituent symbols leads to segmentation errors. The proposed approach handles both the above listed problems in the context of Devanagari script. An attempt is made to identify all possible connected symbols of Devanagari (could be a consonant, vowel, half consonant or conjunct consonant henceforward shall be referred as a basic symbol) in the middle zone without segmenting the conjunct characters. On observing 469580 words from a variety of sources in our study, it is found that only 345 symbols are used more frequently in the middle zone and cover 99.97% of the text. They are then classified into 16 different classes on the basis of structural properties which are invariant across fonts and sizes. To validate the proposed classification scheme, results are presented on 25 fonts and three sizes.
The presentation will describe an algorithm through which one can recognize Devanagari Characters. Devanagari is the script in which Hindi is represented. This algorithm
could automatically segment character from the image of Devenagari text and then recognize them.
For extracting the individual characters from the image of Devanagari text, algorithm segmented the image several
times using the vertical and horizontal projection.
The algorithm starts with first segmenting the lines separately from the document by taking horizontal projection and then the line
into words by taking vertical projection of the line. Another step which is particular to the separation of
Devanagari characters was required and was done by first removing the header line by finding horizontal projection
of each word. The characters can then be extracted by vertical projection of the word without the header line.
Algorithm uses a Kohonen Neural Netowrk for the recognition task. After the separation of the characters from the
image, the image matrix was then downsampled to bring it down to a fixed size so as to make the recognition
size independent. The matrix can then be fed as input neurons to the Kohonen Neural Network and the winning neuron is
found which identifies the recognized the character. This information in Kohonen Neural Network was stored
earlier during the training phase of the neural network. For this, we first assigned random weights from input neurons
to output neurons and then for each training set, the winning neuron was calculated by finding the maximum
output produced by the neurons. The wights for this winning neuron were then adjusted so that it responds to this
pattern more strongly the next time.
Vocabulary length experiments for binary image classification using bov approachsipij
Bag-of-Visual-words (BoV) approach to image classification is popular among computer vision scientists.
The visual words come from the visual vocabulary which is constructed using the key points extracted from
the image database. Unlike the natural language, the length of such vocabulary for image classification is
task dependent. The visual words capture the local invariant features of the image. The region of image
over which a visual word is constrained forms the spatial content for the visual word. Spatial pyramid
representation of images is an approach to handle spatial information. In this paper, we study the role of
vocabulary lengths for the levels of a simple two level spatial pyramid to perform binary classifications.
Two binary classification problems namely to detect the presence of persons and cars are studied. Relevant
images from PASCAL dataset are being used for the learning activities involved in this work
An Improved Approach for Word Ambiguity RemovalWaqas Tariq
Word ambiguity removal is a task of removing ambiguity from a word, i.e. correct sense of word is identified from ambiguous sentences. This paper describes a model that uses Part of Speech tagger and three categories for word sense disambiguation (WSD). Human Computer Interaction is very needful to improve interactions between users and computers. For this, the Supervised and Unsupervised methods are combined. The WSD algorithm is used to find the efficient and accurate sense of a word based on domain information. The accuracy of this work is evaluated with the aim of finding best suitable domain of word. Keywords: Human Computer Interaction, Supervised Training, Unsupervised Learning, Word Ambiguity, Word sense disambiguation
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.
Devnagari handwritten numeral recognition using geometric features and statis...Vikas Dongre
This paper presents a Devnagari Numerical recognition method based on statistical
discriminant functions. 17 geometric features based on pixel connectivity, lines, line directions, holes,
image area, perimeter, eccentricity, solidity, orientation etc. are used for representing the numerals. Five
discriminant functions viz. Linear, Quadratic, Diaglinear, Diagquadratic and Mahalanobis distance are
used for classification. 1500 handwritten numerals are used for training. Another 1500 handwritten
numerals are used for testing. Experimental results show that Linear, Quadratic and Mahalanobis
discriminant functions provide better results. Results of these three Discriminants are fed to a majority
voting type Combination classifier. It is found that Combination classifier offers better results over
individual classifiers.
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.
A bidirectional text transcription of braille for odia, hindi, telugu and eng...eSAT Journals
Abstract
Communication gap establishes an aura of unwillingness of understanding the factor behind. Basically, this is one of the common
agenda for the visually challenged people. In order to bridge this gap, a proper platform of learning for both the mass and the
visually challenged for any native language is emphasized in this paper through Braille pattern. Braille, a code, well known to
visually challenged as their mode of communication is now converted into normal text in Odia, Hindi, Telugu and English
languages using Image segmentation as the base criteria with MATLAB as its simulation field so that every person can be able to
easily decode the information being conveyed by these people. The algorithm makes the best use of segmentation, histogram
analysis, pattern recognition, letter arrays, data base generation with testing in software and dumping in using Spartan 3e FPGA
kit. This paper also elaborates on the reverse conversion of native languages and English to Braille making the paper to be more
compatible. The processing speed with efficiency and accuracy defines the effective features of this paper as a successful
approach in both software and hardware.
Keywords: visually challenge, image segmentation, MATLAB, pattern recognition, Spartan 3e FPGA kit, compatible.
COMPRESSION BASED FACE RECOGNITION USING TRANSFORM DOMAIN FEATURES FUSED AT M...sipij
The physiological biometric trait face images are used to identify a person effectively. In this paper, we
propose compression based face recognition using transform domain features fused at matching level. The
2D images are converted into 1-D vectors using mean to compress number of pixels. The Fast Fourier
Transform (FFT) and Discrete Wavelet Transform (DWT) are used to extract features. The low and high
frequency coefficients of DWT are concatenated to obtained final DWT features. The performance
parameters are computed by comparing database and test image features of FFT and DWT using Euclidian
Distance (ED). The performance parameters of FFT and DWT are fused at matching level to obtain better
results. It is observed that the performance of proposed method is better than the existing methods.
BrailleOCR: An Open Source Document to Braille Converter Applicationpijush15
This presentation is actually about an Open Source application, BrailleOCR that helps to convert scanned documents to Braille and thus helps the Visually Impaired.
What is the use of this application in real life? Well, BrailleOCR is currently the only app that integrated Optical character recognition and Braille Translation together. This app will eventually help converting a lot of important documents to Braille. The project site for this project is given here
IJCA Paper: http://www.ijcaonline.org/archives/volume68/number16/11664-7254
Project site: https://code.google.com/p/brailleocr/
The app uses a four step process. Initially, we have a scanned image, which is a RGB image. The first step or the Pre-Processing step deals with conversion of a RGB image to grayscale. The 2nd step deals with Character Recognition using the Tesseract Engine. Now, the recognition step may have errors and we require post processing to correct them. The 3rd step is thus the Post-Processing step and it actually corrects errors in the previous step. The final and the most important step is the Braille Conversion step.
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.
Elicitation of Apt Human Emotions based on Discrete Wavelet Transform in E-Le...AM Publications
This paper is going to address one of the important challenges faced by e-learning environment that is simulating a real-time class room environment. The success of the class room is mainly because of the special bond the tutor and the student share among themselves in a class room but this affective aspect completely get missed in e-learning environment. Most of the research which is been going on this field have concentrated only on student emotional aspect to some extent leaving completely the emotional aspect of the Teacher. When the most of the educational institution is concentrating on the cognitive aspects of the pedagogy, this paper is strongly advocating on the affective aspect of the pedagogy – which is the need of the hour. This paper deals with how the emotion captured for both the teacher and student by means of energy coefficients calculated by Discrete Wavelet Transform(DWT) method to be used effectively and efficiently in the e-learning platform.
A mediator person is required for communication between deaf person and a second person. But a
mediator should know the sign language used by deaf person. But this is also not possible always since there are
multiple sign languages for multiple languages. It is difficult for a deaf person to understand what a second
person speaks. And therefore deaf person should keep track of lip movements of second person in order to know
what he is speaking. But the lip movements do not give proper efficiency and accuracy since the facial
expressions and speech might not match. To overcome the above problems we have proposed a system, an
Android Application for recognizing sign language using hand gesture with the facility for user to define and
upload their own sign language into the system. The features of this system are the real time conversion of
gesture to text and speech. For two-way communication between deaf person and second person, the speech of
second person is converted into text. The processing steps include: gesture extraction, gesture matching and
conversion of text to speech and vice-versa. The system is not only useful for deaf community but can also be
used by common people who migrate to different regions and do not know local language.
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...IJERA Editor
Gesture recognition system received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. Gesture is one of human body languages which are popularly used in our daily life. It is a communication system that consists of hand movements and facial expressions via communication by actions and sights. This research mainly focuses on the research of gesture extraction and finger segmentation in the gesture recognition. In this paper, we have used image analysis technologies to create an application by encoding in MATLAB program. We will use this application to segment and extract the finger from one specific gesture. This paper is aimed to give gesture recognition in different natural conditions like dark and glare condition, different distances condition and similar object condition then collect the results to calculate the successful extraction rate.
Continuous speech segmentation using local adaptive thresholding technique in...TELKOMNIKA JOURNAL
Continuous speech is a form of natural human speech that is continuous without a clear boundary between words. In continuous speech recognition, a segmentation process is needed to cut the sentence at the boundary of each word. Segmentation becomes an important step because a speech can be recognized from the word segments produced by this process. The segmentation process in this study was carried out using local adaptive thresholding technique in the blocking block area method. This study aims to conduct performance comparisons for five local adaptive thresholding methods (Niblack, Sauvola, Bradley, Guanglei Xiong and Bernsen) in continuous speech segmentation to obtain the best method and optimum parameter values. Based on the results of the study, Niblack method is concluded as the best method for continuous speech segmentation in Indonesian language with the accuracy value of 95%, and the optimum parameter values for such method are window = 75 and k = 0.2.
FREEMAN CODE BASED ONLINE HANDWRITTEN CHARACTER RECOGNITION FOR MALAYALAM USI...acijjournal
Handwritten character recognition is conversion of handwritten text to machine readable and editable form. Online character recognition deals with live conversion of characters. Malayalam is a language spoken by millions of people in the state of Kerala and the union territories of Lakshadweep and Pondicherry in India. It is written mostly in clockwise direction and consists of loops and curves. The method aims at training a simple neural network with three layers using backpropagation algorithm.
Freeman codes are used to represent each character as feature vector. These feature vectors act as inputs to the network during the training and testing phases of the neural network. The output is the character expressed in the Unicode format.
Optimized Biometric System Based on Combination of Face Images and Log Transf...sipij
The biometrics are used to identify a person effectively. In this paper, we propose optimised Face
recognition system based on log transformation and combination of face image features vectors. The face
images are preprocessed using Gaussian filter to enhance the quality of an image. The log transformation
is applied on enhanced image to generate features. The feature vectors of many images of a single person
image are converted into single vector using average arithmetic addition. The Euclidian distance(ED) is
used to compare test image feature vector with database feature vectors to identify a person. It is
experimented that, the performance of proposed algorithm is better compared to existing algorithms.
SENTIMENT ANALYSIS IN MYANMAR LANGUAGE USING CONVOLUTIONAL LSTM NEURAL NETWORKijnlc
In recent years, there has been an increasing use of social media among people in Myanmar and writing review on social media pages about the product, movie, and trip are also popular among people. Moreover, most of the people are going to find the review pages about the product they want to buy before deciding whether they should buy it or not. Extracting and receiving useful reviews over interesting products is very important and time consuming for people. Sentiment analysis is one of the important processes for extracting useful reviews of the products. In this paper, the Convolutional LSTM neural network architecture is proposed to analyse the sentiment classification of cosmetic reviews written in Myanmar Language. The paper also intends to build the cosmetic reviews dataset for deep learning and sentiment lexicon in Myanmar Language.
Sentiment Analysis In Myanmar Language Using Convolutional Lstm Neural Networkkevig
In recent years, there has been an increasing use of social media among people in Myanmar and writing
review on social media pages about the product, movie, and trip are also popular among people. Moreover,
most of the people are going to find the review pages about the product they want to buy before deciding
whether they should buy it or not. Extracting and receiving useful reviews over interesting products is very
important and time consuming for people. Sentiment analysis is one of the important processes for extracting
useful reviews of the products. In this paper, the Convolutional LSTM neural network architecture is
proposed to analyse the sentiment classification of cosmetic reviews written in Myanmar Language. The
paper also intends to build the cosmetic reviews dataset for deep learning and sentiment lexicon in Myanmar
Language.
Devnagari handwritten numeral recognition using geometric features and statis...Vikas Dongre
This paper presents a Devnagari Numerical recognition method based on statistical
discriminant functions. 17 geometric features based on pixel connectivity, lines, line directions, holes,
image area, perimeter, eccentricity, solidity, orientation etc. are used for representing the numerals. Five
discriminant functions viz. Linear, Quadratic, Diaglinear, Diagquadratic and Mahalanobis distance are
used for classification. 1500 handwritten numerals are used for training. Another 1500 handwritten
numerals are used for testing. Experimental results show that Linear, Quadratic and Mahalanobis
discriminant functions provide better results. Results of these three Discriminants are fed to a majority
voting type Combination classifier. It is found that Combination classifier offers better results over
individual classifiers.
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.
A bidirectional text transcription of braille for odia, hindi, telugu and eng...eSAT Journals
Abstract
Communication gap establishes an aura of unwillingness of understanding the factor behind. Basically, this is one of the common
agenda for the visually challenged people. In order to bridge this gap, a proper platform of learning for both the mass and the
visually challenged for any native language is emphasized in this paper through Braille pattern. Braille, a code, well known to
visually challenged as their mode of communication is now converted into normal text in Odia, Hindi, Telugu and English
languages using Image segmentation as the base criteria with MATLAB as its simulation field so that every person can be able to
easily decode the information being conveyed by these people. The algorithm makes the best use of segmentation, histogram
analysis, pattern recognition, letter arrays, data base generation with testing in software and dumping in using Spartan 3e FPGA
kit. This paper also elaborates on the reverse conversion of native languages and English to Braille making the paper to be more
compatible. The processing speed with efficiency and accuracy defines the effective features of this paper as a successful
approach in both software and hardware.
Keywords: visually challenge, image segmentation, MATLAB, pattern recognition, Spartan 3e FPGA kit, compatible.
COMPRESSION BASED FACE RECOGNITION USING TRANSFORM DOMAIN FEATURES FUSED AT M...sipij
The physiological biometric trait face images are used to identify a person effectively. In this paper, we
propose compression based face recognition using transform domain features fused at matching level. The
2D images are converted into 1-D vectors using mean to compress number of pixels. The Fast Fourier
Transform (FFT) and Discrete Wavelet Transform (DWT) are used to extract features. The low and high
frequency coefficients of DWT are concatenated to obtained final DWT features. The performance
parameters are computed by comparing database and test image features of FFT and DWT using Euclidian
Distance (ED). The performance parameters of FFT and DWT are fused at matching level to obtain better
results. It is observed that the performance of proposed method is better than the existing methods.
BrailleOCR: An Open Source Document to Braille Converter Applicationpijush15
This presentation is actually about an Open Source application, BrailleOCR that helps to convert scanned documents to Braille and thus helps the Visually Impaired.
What is the use of this application in real life? Well, BrailleOCR is currently the only app that integrated Optical character recognition and Braille Translation together. This app will eventually help converting a lot of important documents to Braille. The project site for this project is given here
IJCA Paper: http://www.ijcaonline.org/archives/volume68/number16/11664-7254
Project site: https://code.google.com/p/brailleocr/
The app uses a four step process. Initially, we have a scanned image, which is a RGB image. The first step or the Pre-Processing step deals with conversion of a RGB image to grayscale. The 2nd step deals with Character Recognition using the Tesseract Engine. Now, the recognition step may have errors and we require post processing to correct them. The 3rd step is thus the Post-Processing step and it actually corrects errors in the previous step. The final and the most important step is the Braille Conversion step.
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.
Elicitation of Apt Human Emotions based on Discrete Wavelet Transform in E-Le...AM Publications
This paper is going to address one of the important challenges faced by e-learning environment that is simulating a real-time class room environment. The success of the class room is mainly because of the special bond the tutor and the student share among themselves in a class room but this affective aspect completely get missed in e-learning environment. Most of the research which is been going on this field have concentrated only on student emotional aspect to some extent leaving completely the emotional aspect of the Teacher. When the most of the educational institution is concentrating on the cognitive aspects of the pedagogy, this paper is strongly advocating on the affective aspect of the pedagogy – which is the need of the hour. This paper deals with how the emotion captured for both the teacher and student by means of energy coefficients calculated by Discrete Wavelet Transform(DWT) method to be used effectively and efficiently in the e-learning platform.
A mediator person is required for communication between deaf person and a second person. But a
mediator should know the sign language used by deaf person. But this is also not possible always since there are
multiple sign languages for multiple languages. It is difficult for a deaf person to understand what a second
person speaks. And therefore deaf person should keep track of lip movements of second person in order to know
what he is speaking. But the lip movements do not give proper efficiency and accuracy since the facial
expressions and speech might not match. To overcome the above problems we have proposed a system, an
Android Application for recognizing sign language using hand gesture with the facility for user to define and
upload their own sign language into the system. The features of this system are the real time conversion of
gesture to text and speech. For two-way communication between deaf person and second person, the speech of
second person is converted into text. The processing steps include: gesture extraction, gesture matching and
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neural network with backpropagation algorithm. System deals with images of bare hands to achieve the
recognition task. For each individual sign 10 sample images have been considered, which means in
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considered sample images, are converted into feature vectors. Experimental results reveal that this can
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recognition task. For each individual sign 10 sample images have been considered, which means in
total300 samples have been processed. In order to compare between the training set of signs and the
considered sample images, are converted into feature vectors. Experimental results reveal that this can
recognize selected ASL signs (accuracy of 92.00%). Finally, the system has been implemented issuing hand
gesture commands for ASL to a robot car, named “Moto-robo”.
A SIGNATURE BASED DRAVIDIAN SIGN LANGUAGE RECOGNITION BY SPARSE REPRESENTATIONijnlc
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segmentation techniques. The results found were outstanding with average accuracy for both line and word. It
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AN APPORACH FOR SCRIPT IDENTIFICATION IN PRINTED TRILINGUAL DOCUMENTS USING T...ijaia
In this work, we review the outcome of texture features for script classification. Rectangular White Space
analysis algorithm is used to analyze and identify heterogeneous layouts of document images. The texture
features, namely the color texture moments, Local binary pattern (LBP) and responses of Gabor, LM-filter,
S-filter, R-filter are extracted, and combinations of these are considered in the classification. In this work,
a probabilistic neural network and Nearest Neighbor are used for classification. To corrabate the
adequacy of the proposed strategy, an experiment was operated on our own data set. To study the effect of
classification accuracy, we vary the database sizes and the results show that the combination of multiple
features vastly improves the performance.
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Gesture recognition system received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. Gesture is one of human body languages which are popularly used in our daily life. It is a communication system that consists of hand movements and facial expressions via communication by actions and sights. This research mainly focuses on the research of gesture extraction and finger segmentation in the gesture recognition. In this paper, we have used image analysis technologies to create an application by encoding in MATLAB program. We will use this application to segment and extract the finger from one specific gesture. This paper is aimed to give gesture recognition in different natural conditions like dark and glare condition, different distances condition and similar object condition then collect the results to calculate the successful extraction rate.
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PERFORMANCE EVALUATION OF STATISTICAL CLASSIFIERS USING INDIAN SIGN LANGUAGE DATASETS
1. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.5, October 2011
DOI : 10.5121/ijcsea.2011.1513 167
PERFORMANCE EVALUATION OF
STATISTICAL CLASSIFIERS USING INDIAN
SIGN LANGUAGE DATASETS
M.Krishnaveni1
and V.Radha2
1,2
Department of Computer Science, Avinashilingam Institute for Home Science and
Higher Education for Women, Coimbatore, India
krishnaveni.rd@gmail.com
radharesearch@yahoo.com
ABSTRACT
Sign language is the key for communication between deaf people. The significance of sign language is
accentuated by various research activities and the technical aspects will definitely improve the
communication needs. General view based sign language recognition systems extract manual parameters
by a single camera view because it seems to be user friendly and hardware complexity; however it needs a
high accuracy classifier for classification and recognition purpose. The decision making of the system in
this work employs Indian sign language datasets and the performance evaluation of the system is compared
by deploying the K-NN, Naïve Bayes and PNN classifiers. Classification using an instance-based classifier
can be a simple matter of locating the instance space and labelling the unknown instance with the same
class label as that of the located (known) neighbour. Classifier always tries to improve the classification
rate by pushing classifiers into an optimised structure. In each hand posture, a measure of properties like
area, mean intensity, centroid, perimeter and diameter are taken; the classifier then uses these properties
to determine the sign in different angles. They estimate the probability that a sign belongs to each of the
target classes that is fixed. The impact of such study may reflect the exploration for using such algorithms
in other similar applications such as text classification and the development of automated systems.
KEYWORDS
Sign Language, PNN, K-NN classifier, Navie Bayes, performance rate
1. INTRODUCTION
Sign language is important in humankind that is showing an increasing research interest in
eradicating barriers faced by differently abled people in communicating and contributing to the
society [1]. Automation over sign language recognition systems can greatly facilitate the vocal
and the non-vocal communities which can be equivalently best and successive as speech-
recognition systems [2]. Despite common misconceptions, sign languages are complete natural
languages, with their own syntax and grammar. However, sign languages are not universal. As is
the case in spoken language, every country has got its own sign language with high degree of
grammatical variations. The sign language used in India is commonly known as Indian Sign
Language (henceforth called ISL)[6].Linguistic studies on ISL were started around 1978 and it
has been found that ISL is a complete natural language, instigated in India, having its own
morphology, phonology, syntax, and grammar. ISL is not only used by the deaf people but also
2. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.5, October 2011
168
by the hearing parents of the deaf children, the hearing children of deaf adults and hearing deaf
educators. Therefore the need to build a automation system that can associate signs to the words
of spoken language, and which can further be used to learn ISL, is significant. This paper
proposes a system for recognizing Indian sign language using the view based approach and the
statistical classifier algorithm as a learning tool. These classifiers take the advantage of accuracy,
ease of implementation and operational robustness. The paper is arranged as follows. Section 2
gives a brief overview of Indian sign language. Section 3 describes the feature extraction
methodology, by using boundary detection techniques. Section 4 deals with the short description
of classifiers used. Section 5 gives the performance evaluation of the classifiers using ISL
datasets. Finally, section 6 summarizes the framework and future work that can be adopted.
2. OVERVIEW OF ISL CLASSIFICATION SYSTEM
Indian Sign languages are rich, faceted language, and their full complexity is beyond current
gesture recognition technologies [6]. The interpersonal communication problem between signer
and hearing community could be resolved by building up a new communication bridge
integrating components for sign[5]. Several Significant problems specific to Automatic Sign
Recognition are i) Distinguishing gestures from signs ii) Context dependency (directional, verbs,
inflections, etc) iii) Basic unit modeling (how do we describe them?) iii) Transitions between
signs (Movements) iv) Repetition (Cycles of movement). The ISL dictionary is been build by Sri
Ramakrishna mission Vidyalaya College of Education, Coimbatore which as split the ISL into
five parameters namely handshapes, locations, Orientation, Location, Movements and Facial
Expression. Figure 1 explains the framework for classification system.
Figure 1: Steps involved for classification methodology for Indian sign datasets
3. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.5, October 2011
169
3. FEATURE EXTRACTION METHODOLOGY
The great variability in gestures and signs, both in time, size, and position, as well as
interpersonal differences, makes the recognition task difficult [11]. By extracting features from
image processing sequence classification can be done by a discriminative classifier. Gestures,
particularly in sign language, involve significant motion of the hands[5]. Thus, in developing a
sign language recognition system, it is important to model both the motion (temporal
characteristics) and shape (spatial characteristics) of the hand. In this research work only the
spatial characteristics of the hand are of concern. Figure 2 shows the schematic view of gesture
recognition process.
Figure 2: Schematic view of gesture recognition process
Feature extraction is the process of generating a set of descriptors or characteristic attributes from
a binary image. Most of the features used in existing sign language recognition systems focus
only on one aspect of the signing like hand movements or facial expressions. Figure 3 shows the
feature extraction concept.
Figure 3: Hand feature extraction
The strategy used for segmentation is boundary segmentation method which traces the exterior
boundaries of objects, as well as boundaries of holes inside these objects, in the binary image [3].
The two important need for this method of sign extraction is First, it keeps the overall complexity
of the segmentation process low. Secondly, it eliminates candidates that may produce smooth
continuations, but otherwise are inconsistent with the segments in the image. This simplifies the
decisions made by the segmentation process and generally leads to more accurate
segmentation[7]. The feature extracted from the database used is mean intensity, area, perimeter,
diameter, centroid.
Mean Intensity: The mean intensity µ in the selected region of interest (ROI) is given in eqn
(1):
,
1
( , )
x y
R O I x y d x d y
N
µ = ∫ ………………..(1)
Area: The area of an object is a relatively noise-immune measure of object size, because every
pixel in the object contributes towards the measurement. The area can be defined using eqn (2)
( ) ( , )A S I x y d y d x= ∫ ∫ ………………………..(2)
Where, I(x,y) = 1 if the pixel is within a shape,(x,y)∈S,
4. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.5, October 2011
170
0 otherwise.
Perimeter: The perimeter measurement gives the smooth relative boundaries and the perimeter
of the region is defined as by the eqn (3)
2 2
( ) ( ) ( )
t
P S x t y t d t= +∫ …………………...(3)
Diameter: The distance around a selected region is called the circumference. The distance across
a circle through the center is called the diameter. is the radius of the circumference of a circle..
Thus, for any circle, if you divide the circumference by the diameter, you get a value close to .
This relationship is expressed in the following eqn (4):
c/d=п …………………………………..(4)
where,C is circumference and d is diameter
Centroid: It specifies the center of mass of the region. Centroid is the horizontal coordinate (or x-
coordinate) of the center of mass, and the second element is the vertical coordinate (or y-
coordinate) and it is written as given in eqn (5)
…………………………………………………………(5)
4. SHORT DESCRIPTION ON CLASSIFIERS
Classification (generalization) using an instance-based classifier can be a simple matter of
locating the nearest neighbour in instance space and labelling the unknown instance with the
same class label as that of the located (known) neighbour [4]. This approach is often referred to as
a neighbour classifier. Classifier always tries to improve the classification rate by pushing
classifiers into an optimised structure[10].
4.1 K-NN Classifier
The K-Nearest Neighbor classifier is a supervised learning algorithm where the result of a new
instance query is classified based on majority of the K-nearest neighbor category. The purpose of
this algorithm is to classify a new object based on attributes and training samples[8]. The Nearest
neighbor classifier is based on learning by analogy. The training samples are described by n-
dimensional numeric attributes. Each sample represents a point in an n-dimensional pattern space.
More robust models can be achieved by locating k, where k > 1, neighbours and letting the
majority vote decide the outcome of the class labelling. A higher value of k results in a smoother,
less locally sensitive, function. The Nearest Neighbor classifier can be regarded as a special case
of the more general K-Nearest Neighbors classifier, hereafter referred to as a K-NN classifier.
The data for KNN algorithm consists of several multivariate attributes names Xi that will be used
to classify the object Y. The data of KNN can have any measurement scale from ordinal,
nominal, to quantitative scale, this study deals only with quantitative Xi and binary (nominal) Y.
All training samples are included as nearest neighbors if the distance of this training sample to the
query is less than or equal to the Kth
smallest distance. In other words, the distances are sorted of
all training samples to the query and determine the Kth
minimum distance. The unknown sample
is assigned the most common class among its K-Nearest Neighbors. These K training samples are
the closest k nearest neighbors for the unknown sample. Closeness is defined in terms of
Euclidean distance, where the Euclidean between two points, X = (x1, x2,….xn) and Y=
(y1,y2,…yn) is given in eqn (6)
5. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.5, October 2011
171
The drawback of increasing the value of k is when k approaches n, where n is the size of the
instance base, the performance of the classifier will approach that of the most straightforward
statistical baseline, the assumption that all unknown instances belong to the class most frequently
represented in the training data. The high degree of local sensitivity makes nearest neighbor
classifiers highly susceptible to noise in the training data.
4.2 Naive bayesian classifier
A Naive Bayes classifier assigns a new observation to the most probable class, assuming the
features are conditionally independent given the class value [9]. It can outperform more
sophisticated classification methods by categorizing incoming objects to their appropriate class.
The Naive Bayesian classifiers can handle a random number of independent variables whether
continuous or categorical.
It classifies data in two steps:
1. Training step: Using the training samples, the method estimates the parameters of a probability
distribution, assuming features are conditionally independent given the class.
2. Prediction step: For any unseen test sample, the method computes the posterior probability of
that sample belonging to each class. The method then classifies the test sample according the
largest posterior probability.
Bayes theorem used, takes the eqn as given in (7) and (8)
P (H|X) = P(X|H) P(H) / P(X) ………..(7)
It can also be expressed as …………………(8)
Where P(X) is constant for all classes, only P(X|Ci)P(Ci) need be maximized.
The class-conditional independence assumption greatly simplifies the training step since
estimation can be done using one-dimensional class-conditional density for each feature
individually. This assumption of class independence allows the Naive Bayes classifier to better
estimate the parameters required for accurate classification while using less training data than
many other classifiers. This makes it particularly effective for datasets containing many
predictors or features. Since the build process for Naive Bayes is parallelized it can be used for
both binary and multiclass classification problems.
4.3 Probabilistic Neural Network
Probabilistic neural networks are forward feed networks built with three layers. They train
quickly since the training is done in one pass of each training vector, rather than several.
Probabilistic neural networks estimate the probability density function for each class based on the
training samples. The probabilistic neural network uses Parzen or a similar probability density
function. This is calculated for each test vector. This is what is used in the dot product against the
input vector as described below. Usually a spherical Gaussian basis function is used, although
many other functions work equally well. Vectors must be normalized prior to input into the
network. There is an input unit for each dimension in the vector. The input layer is fully
connected to the hidden layer. The hidden layer has a node for each classification. Each hidden
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node calculates the dot product of the input vector with a test vector subtracts 1 from it and
divides the result by the standard deviation squared. The output layer has a node for each pattern
classification. The sum for each hidden node is sent to the output layer and the highest values
wins.The Probabilistic neural network trains immediately but execution time is slow and it
requires a large amount of space in memory. It really only works for classifying data. The training
set must be a thorough representation of the data. Probabilistic neural networks handle data that
has spikes and points outside the norm better than other neural nets. PNN has proven to be more
time efficient than conventional back propagation based networks. In order to classify a feature
pattern vector x that is to assign the pattern to one among k predefined classes, the
conditional density of each class is estimated since it represents the uncertainty
associated by the rule of bayes that allow making optimal decision[12]. One possible way of
looking at this technique is to build sphere of influence p(s,x) around each training sample s and
to add them up for each of k classes.
........................................ (9)
5. PERFORMANCE EVALUATION
The proposed classifier Navie bayes is used to justify the objects using new methods to get a
maximum accuracy comparing to K-NN and PNN classifier. The features are the parameters
extracted from the sign images which are taken from normal camera. In each image, a measure of
properties is taken to determine the sign in different position. They estimate the probability that a
sign belongs to each of the target classes that is predetermined. In the training phase, the training
set is used to decide how the parameters must be weighted and combined in order to separate the
various classes of signs. The classifier used for classification is validated using sensitivity,
specificity error rate, predictive value, likelihood value, plotting the classification and
misclassification error rate according to the sample datasets.
Table 1 : Performance evaluation of NB ,KNN and PNN Classifiers
To keep track of the performance during the validation of classifiers Table 1 mentioned values
are the measures taken into consideration. Conducted experiments with Indian sign language
S.No Performance
Measures
NB KNN PNN
1. Classified rate 1.0000 0.9500 1.0000
2. Sensitivity 0.7143 0.5714 0.5000
3. Specificity 0.8571 0.6857 1
4. Error rate 0.3429 0.6857 0.4000
5. Inconclusive rate 0 0 0
6. Positive predictive
value
0.5556 0.3077 1
7. Negative
Predictive Value
0.9231 0.8636 0.7500
8. Negative
likelihood
0.3333 0.6316 0.5000
9. Positive Likelihood 5.000 1.7778 1.8650
10. Prevalence 0.2000 0.2000 0.4000
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datasets shows that the Navie bayes classifier has better performance than K-NN, PNN and the
current implementation for both classifiers yield good results when compared to other
implementations by other researchers.
0 5 10 15 20 25 30 35
1
1.5
2
2.5
3
3.5
4
4.5
5
Number of Samples
TargetClasses
NB Class Performnace
target
class
Figure 4: Navie Bayes class Performance
0 5 10 15 20 25 30 35
1
1.5
2
2.5
3
3.5
4
4.5
5
Number of Samples
TargetClasses
KNN Class performance
Target
Class
Figure 5: KNN class Performance
8. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.5, October 2011
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Figure 6 : PNN class Performance
Figure 4,5 and 6 depicts the classifier performance based on the datasets taken and the target class
is less achieved by K-NN and PNN classifier than the proposed Navie bayes classifier.
6. CONCLUSION
An analysis of different classifiers is done in which the Navie bayes approach is proved
to be better for sign language classification system. This help in understanding the current
state of knowledge in Indian sign language research area. And therefore, identifying the
directions lead towards the standardised procedure of classifier system design. However, the
results in this work are biased by the size of the database: on the one hand, the lack of training
data and the large amount of singletons leads to a very difficult task. The methods used in this
work are focused towards good recognition accuracy and not toward real-time performance and
thus it meets the sample experimental requirements. This research result developed here is a
principled technique that will enable their use, not only in sign language or hand gesture
recognition but also in other related areas of computer vision.
Acknowledgments. The authors thank RamaKrishna Mission Vidyalaya, International Human
Resource Development Centre (IHRDC for the disabled) for providing Indian sign language
dictionary.
REFERENCES
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Artificial Neural Network. Faculty of Information and Communication Technology,Universiti Tunku
Abdul Rahman (UTAR), MALAYSIA.tensaix2j@yahoo.com, tayyh@mail.utar.edu.my
[2] Noor Saliza Mohd Salleh, Jamilin Jais, Lucyantie Mazalan, Roslan Ismail, Salman Yussof, Azhana
Ahmad, Adzly Anuar, Dzulkifli Mohamad (2006) Sign Language to Voice Recognition: Hand
Detection Techniques for Vision-Based Approach . Current Developments in Technology-Assisted
Education .