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Showing papers on "Sketch recognition published in 2012"


Journal ArticleDOI
01 Jul 2012
TL;DR: This paper is the first large scale exploration of human sketches, developing a bag-of-features sketch representation and using multi-class support vector machines, trained on the sketch dataset, to classify sketches.
Abstract: Humans have used sketching to depict our visual world since prehistoric times. Even today, sketching is possibly the only rendering technique readily available to all humans. This paper is the first large scale exploration of human sketches. We analyze the distribution of non-expert sketches of everyday objects such as 'teapot' or 'car'. We ask humans to sketch objects of a given category and gather 20,000 unique sketches evenly distributed over 250 object categories. With this dataset we perform a perceptual study and find that humans can correctly identify the object category of a sketch 73% of the time. We compare human performance against computational recognition methods. We develop a bag-of-features sketch representation and use multi-class support vector machines, trained on our sketch dataset, to classify sketches. The resulting recognition method is able to identify unknown sketches with 56% accuracy (chance is 0.4%). Based on the computational model, we demonstrate an interactive sketch recognition system. We release the complete crowd-sourced dataset of sketches to the community.

874 citations


Proceedings ArticleDOI
18 Oct 2012
TL;DR: This paper proposes a real-time system for dynamic hand gesture recognition based on action graph, which shares similar robust properties with standard HMM but requires less training data by allowing states shared among different gestures.
Abstract: Recent advances in depth sensing provide exciting opportunities for the development of new methods for human activity understanding. Yet, little work has been done in the area of hand gesture recognition which has many practical applications. In this paper we propose a real-time system for dynamic hand gesture recognition. It is fully automatic and robust to variations in speed and style as well as in hand orientations. Our approach is based on action graph, which shares similar robust properties with standard HMM but requires less training data by allowing states shared among different gestures. To deal with hand orientations, we have developed a new technique for hand segmentation and orientation normalization. The proposed system is evaluated on a challenging dataset of twelve dynamic American Sign Language (ASL) gestures.

273 citations


BookDOI
01 Jan 2012
TL;DR: The completely counter-intuitive result that by working with a very few points distant from the mean, one can obtain remarkable classification accuracies is shown, and if these points are determined by the Order Statistics of the distributions, the accuracy of the method, referred to as Classification by Moments of Order Statistics (CMOS), attains the optimal Bayes’ bound.
Abstract: The gold standard for a classifier is the condition of optimality attained by the Bayesian classifier. Within a Bayesian paradigm, if we are allowed to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be to achieve this based on the (Mahalanobis) distance from the corresponding means. The reader should observe that, in this context, the mean, in one sense, is the most central point in the respective distribution. In this paper, we shall show that we can obtain optimal results by operating in a diametrically opposite way, i.e., a so-called “anti-Bayesian” manner. Indeed, we shall show the completely counter-intuitive result that by working with a very few (sometimes as small as two) points distant from the mean, one can obtain remarkable classification accuracies. Further, if these points are determined by the Order Statistics of the distributions, the accuracy of our method, referred to as Classification by Moments of Order Statistics (CMOS), attains the optimal Bayes’ bound! This claim, which is totally counter-intuitive, has been proven for many uni-dimensional, and some multi-dimensional distributions within the exponential family, and the theoretical results have been verified by rigorous experimental testing. Apart from the fact that these results are quite fascinating and pioneering in their own right, they also give a theoretical foundation for the families of Border Identification (BI) algorithms reported in the literature.

159 citations


Journal ArticleDOI
TL;DR: An automated algorithm to extract discriminating information from local regions of both sketches and digital face images is presented and yields better identification performance compared to existing face recognition algorithms and two commercial face recognition systems.
Abstract: One of the important cues in solving crimes and apprehending criminals is matching sketches with digital face images. This paper presents an automated algorithm to extract discriminating information from local regions of both sketches and digital face images. Structural information along with minute details present in local facial regions are encoded using multiscale circular Weber's local descriptor. Further, an evolutionary memetic optimization algorithm is proposed to assign optimal weight to every local facial region to boost the identification performance. Since forensic sketches or digital face images can be of poor quality, a preprocessing technique is used to enhance the quality of images and improve the identification performance. Comprehensive experimental evaluation on different sketch databases show that the proposed algorithm yields better identification performance compared to existing face recognition algorithms and two commercial face recognition systems.

114 citations


Book ChapterDOI
07 Oct 2012
TL;DR: A real-time solution is presented to automatically segment a user's sketch during his/her drawing using a graph-based sketch segmentation algorithm and a semantic-based approach to simulate the past experience in the perceptual system by leveraging a web-scale clipart database.
Abstract: In this paper, we study the problem of how to segment a freehand sketch at the object level. By carefully considering the basic principles of human perceptual organization, a real-time solution is presented to automatically segment a user's sketch during his/her drawing. First, a graph-based sketch segmentation algorithm is proposed to segment a cluttered sketch into multiple parts based on the factor of proximity. Then, to improve the ability of detecting semantically meaningful objects, a semantic-based approach is introduced to simulate the past experience in the perceptual system by leveraging a web-scale clipart database. Finally, other important factors learnt from past experience, such as similarity, symmetry, direction, and closure, are also taken into account to make the approach more robust and practical. The proposed sketch segmentation framework has ability to handle complex sketches with overlapped objects. Extensive experimental results show the effectiveness of the proposed framework and algorithms.

64 citations


Proceedings ArticleDOI
05 May 2012
TL;DR: This work presents QuickDraw, a prototype sketch-based drawing tool, that facilitates drawing of precise geometry diagrams that are often drawn by students and academics in several scientific disciplines and enables users to draw precise diagrams faster than the majority of existing tools in some cases, while having them make fewer corrections.
Abstract: We present QuickDraw, a prototype sketch-based drawing tool, that facilitates drawing of precise geometry diagrams that are often drawn by students and academics in several scientific disciplines. Quickdraw can recognize sketched diagrams containing components such as line segments and circles, infer geometric constraints relating recognized components, and use this information to beautify the sketched diagram. Beautification is based on a novel algorithm that iteratively computes various sub-components of the components using an extensible set of deductive rules. We conducted a user study comparing QuickDraw with four state-of-the-art diagramming tools: Microsoft PowerPoint, Cabri II Plus, Geometry Expressions and Geometer's SketchPad. Our study demonstrates a strong interest among participants for the use of sketch-based software for drawing geometric diagrams. We also found that QuickDraw enables users to draw precise diagrams faster than the majority of existing tools in some cases, while having them make fewer corrections.

62 citations


Proceedings ArticleDOI
09 Jul 2012
TL;DR: A new face descriptor based on gradient orientations to reduce the modality difference in feature extraction stage, called Histogram of Averaged Oriented Gradients (HAOG).
Abstract: Automatic face sketch recognition plays an important role in law enforcement. Recently, various methods have been proposed to address the problem of face sketch recognition by matching face photos and sketches, which are of different modalities. However, their performance is strongly affected by the modality difference between sketches and photos. In this paper, we propose a new face descriptor based on gradient orientations to reduce the modality difference in feature extraction stage, called Histogram of Averaged Oriented Gradients (HAOG). Experiments on CUFS database show that the new descriptor outperforms the state-of-the-art approaches.

57 citations


Journal ArticleDOI
Wei Wu1, Zheng Liu2, Mo Chen1, Xiaomin Yang1, Xiaohai He1 
TL;DR: An automatic container-code recognition system is developed by using computer vision to segment characters for various imaging conditions and the efficiency and effectiveness of the proposed technique for practical usage are demonstrated.
Abstract: Highlights? An automatic container-code recognition system is developed by using computer vision. ? The characteristics of characters are made full use of to locate container-code. ? A two-step method is proposed to segment characters for various imaging conditions. Automatic container-code recognition is of great importance to the modern container management system. Similar techniques have been proposed for vehicle license plate recognition in past decades. Compared with license plate recognition, automatic container-code recognition faces more challenges due to the severity of nonuniform illumination and invalidation of color information. In this paper, a computer vision based container-code recognition technique is proposed. The system consists of three function modules, namely location, isolation, and character recognition. In location module, we propose a text-line region location algorithm, which takes into account the characteristics of single character as well as the spatial relationship between successive characters. This module locates the text-line regions by using a horizontal high-pass filter and scanline analysis. To resolve nonuniform illumination, a two-step procedure is applied to segment container-code characters, and a projection process is adopted to isolate characters in the isolation module. In character recognition module, the character recognition is achieved by classifying the extracted features, which represent the character image, with trained support vector machines (SVMs). The experimental results demonstrate the efficiency and effectiveness of the proposed technique for practical usage.

53 citations


Proceedings ArticleDOI
01 Sep 2012
TL;DR: A new face descriptor to directly match face photos and sketches of different modalities, called Local Radon Binary Pattern (LRBP), inspired by the fact that the shape of a face photo and its corresponding sketch is similar, even when the sketch is exaggerated by an artist.
Abstract: In this paper, we propose a new face descriptor to directly match face photos and sketches of different modalities, called Local Radon Binary Pattern (LRBP). LRBP is inspired by the fact that the shape of a face photo and its corresponding sketch is similar, even when the sketch is exaggerated by an artist. Therefore, the shape of face can be exploited to compute features which are robust against modality differences between face photo and sketch. In LRBP framework, the characteristics of face shape are captured by transforming face image into Radon space. Then, micro-information of face shape in new space is encoded by Local Binary Pattern (LBP). Finally, LRBP is computed by concatenating histograms of local LBPs. In order to capture both local and global characteristics of face shape, LRBP is extracted in a spatial pyramid fashion. Experiments on CUFS and CUFSF datasets indicate the efficiency of LRBP for face sketch recognition.

50 citations


Proceedings ArticleDOI
26 Nov 2012
TL;DR: This paper provides a summary of previous surveys done in this area and focuses on the different application domain which employs hand gestures for efficient interaction, and provides an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it based on different parameters.
Abstract: The ultimate aim is to bring Human Computer Interaction to a regime where interactions with computers will be as natural as an interaction between humans, and to this end, incorporating gestures in HCI is an important research area Gestures have long been considered as an interaction technique that can potentially deliver more natural, creative and intuitive methods for communicating with our computers This paper provides a summary of previous surveys done in this area and focuses on the different application domain which employs hand gestures for efficient interaction The use of hand gestures as a natural interface serves as a motivating force for research in gesture taxonomies, its representations and recognition techniques Also provides an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it based on different parameters The main goal of this survey is to provide researchers in the field with a summary of progress achieved to date and to help identify areas where further research is needed

47 citations


Proceedings ArticleDOI
01 Dec 2012
TL;DR: It is proposed that number of finger tips and the distance of fingertips from the centroid of the hand can be used along with PCA for robustness and efficient results and recognition with neural networks is proposed.
Abstract: Understanding human motions can be posed as a pattern recognition problem. Applications of pattern recognition in information processing problems are diverse ranging from Speech, Handwritten character recognition to medical research and astronomy. Humans express time-varying motion patterns (gestures), such as a wave, in order to convey a message to a recipient. If a computer can detect and distinguish these human motion patterns, the desired message can be reconstructed, and the computer can respond appropriately. This paper represents a framework for a human computer interface capable of recognizing gestures from the Indian sign language. The complexity of Indian sign language recognition system increases due to the involvement of both the hands and also the overlapping of the hands. Alphabets and numbers have been recognized successfully. This system can be extended for words and sentences Recognition is done with PCA (Principal Component analysis). This paper also proposes recognition with neural networks. Further it is proposed that number of finger tips and the distance of fingertips from the centroid of the hand can be used along with PCA for robustness and efficient results.

26 Mar 2012
TL;DR: An automated algorithm that discriminating information from local regions of both sketches and digital face images is presented, using multi-scale circular Weber's Local descriptor to boost the identification performance.
Abstract: One of the important cues in solving crimes and apprehending criminals is matching sketches with digital f ace images. This paper presents an automated algorithm that ext racts discriminating information from local regions of both sketches and digital face images. Structural information along with the minute details present in local facial regions are encod ed using multi-scale circular Weber’s Local descriptor. Further, an evolutionary memetic optimization is proposed to assign op timal weights to every local facial region to boost the identificat ion performance. Since, forensic sketches or digital face imag es can be of poor quality, a pre-processing technique is used to enhance the quality of images and improve the identificationperformance. Comprehensive experimental evaluation on diffe r nt sketch databases show that the proposed algorithm yields be tt r identification performance compared to existing algorithms and two commercial face recognition systems.

Journal ArticleDOI
TL;DR: The present research work focuses on to design and develops a practical framework for real time hand gesture recognition, which has the potential to provide more natural, non-contact solutions.
Abstract: With the increasing use of computing devices in day to day life, the need of user friendly interfaces has lead towards the evolution of different types of interfaces for human computer interaction. Real time vision based hand gesture recognition affords users the ability to interact with computers in more natural and intuitive ways. Direct use of hands as an input device is an attractive method which can communicate much more information by itself in comparison to mice, joysticks etc allowing a greater number of recognition system that can be used in a variety of human computer interaction applications. The gesture recognition system consist of three main modules like hand segmentation, hand tracking and gesture recognition from hand features. The designed system further integrated with different applications like image browser, virtual game etc. possibilities for human computer interaction. Computer Vision based systems has the potential to provide more natural, non-contact solutions. The present research work focuses on to design and develops a practical framework for real time hand gesture

Proceedings ArticleDOI
29 Oct 2012
TL;DR: A query-adaptive shape topic model is proposed to mine object topics and shape topics related to the sketch, in which, multiple layers of information such as sketch, object, shape, image, and semantic labels are modeled in a generative process.
Abstract: In this work, we study the problem of hand-drawn sketch recognition. Due to large intra-class variations presented in hand-drawn sketches, most of existing work was limited to a particular domain or limited pre-defined classes. Different from existing work, we target at developing a general sketch recognition system, to recognize any semantically meaningful object that a child can recognize. To increase the recognition coverage, a web-scale clipart image collection is leveraged as the knowledge base of the recognition system. To alleviate the problems of intra-class shape variation and inter-class shape ambiguity in this unconstrained situation, a query-adaptive shape topic model is proposed to mine object topics and shape topics related to the sketch, in which, multiple layers of information such as sketch, object, shape, image, and semantic labels are modeled in a generative process. Besides sketch recognition, the proposed topic model can also be used for related applications such as sketch tagging, image tagging, and sketch-based image search. Extensive experiments on different applications show the effectiveness of the proposed topic model and the recognition system.

Journal ArticleDOI
TL;DR: A sketch auto-completion framework that addresses challenges of classifying sketched symbols before they are fully completed by learning visual appearances of partial drawings through semi-supervised clustering, followed by a supervised classification step that determines object classes.

Proceedings ArticleDOI
19 Apr 2012
TL;DR: By applying a range of image processing techniques it is demonstrated that the performance is highly dependent on the type of pre-processing steps used and that Equal Error Rates of the Eigenface and Fisherface methods can be reduced using the method proposed in this paper.
Abstract: With many applications in various domains, Face Recognition technology has received a great deal of attention over the decades in the field of image analysis and computer vision. It has been studied by scientists from different areas of psychophysical sciences and those from different areas of computer science. Psychologists and neuro-scientists mainly deal with the human perception part of the topic where as engineers studying on machine recognition of human faces deal with the computational aspects of Face Recognition. Face Recognition is an important and natural human ability of a human being. However developing a computer algorithm to do the same thing is one of the toughest tasks in computer vision. Research over the past several years enables similar recognitions automatically. Various face recognition techniques are represented through various classifications such as, Image-based face recognition and Video-based recognition, Appearance-based and Model-based, 2D and 3D face recognition methods. This paper gives a review of different face recognition techniques available as of today. The focus is on subspace techniques, investigating the use of image pre-processing applied as a preliminary step in order to reduce error rates. The Principle Component Analysis, Linear Discriminant Analysis and their modified methods of face recognition are implemented under subspace techniques, computing False Acceptance Rates (FAR)and False Rejection Rates (FRR) on a standard test set of images that pose typical difficulties for recognition. By applying a range of image processing techniques it is demonstrated that the performance is highly dependent on the type of pre-processing steps used and that Equal Error Rates (EER) of the Eigenface and Fisherface methods can be reduced using the method proposed in this paper.

Proceedings ArticleDOI
04 Jun 2012
TL;DR: EyeSeeYou is designed, a sketch recognition system that teaches users to draw eyes using a simple drawing technique and provides rigorous feedback to create a constructive learning environment to aid the user in improving her drawing.
Abstract: Drawing is a common form of communication and a means of artistic expression. Many of us believe that the ability to draw accurate representations of objects is a skill that either comes naturally or is the result of hours of study or practice or both. As a result many people become intimidated when confronted with the task of drawing. Many books and websites have been developed to teach people step-by-step skills to draw various objects, but they lack the live feedback of a human examiner. We designed EyeSeeYou, a sketch recognition system that teaches users to draw eyes using a simple drawing technique. The system automatically evaluates the freehand drawn sketch of an eye at various stages during creation. We conducted frequent evaluations of the system in order to take an iterative development approach based on user feedback. Our system balances the flexibility of free-hand drawing with step-by-step instructions and realtime assessment. It also provides rigorous feedback to create a constructive learning environment to aid the user in improving her drawing. This paper describes the implementation details of the sketch recognition system. A similar implementation method could be used to provide sketching tutorials for a wide number of images.

Journal ArticleDOI
TL;DR: A new method to translate 3D data reconstructed from a free-hand 2D sketch into an editable form that reflects design intent so that the translated data can be directly used in 3D mechanical CAD systems is presented.
Abstract: In the concept development phase, a designer wants to rapidly create a product's overall shape and user interfaces by making simple sketches, known as 'thumbnail sketches', to evaluate ideas and possibilities. Free-hand 2D sketch systems are regarded as a computerized tool for making thumbnail sketches. After the concept development phase, subsequent editing of the 3D data reconstructed from a free-hand 2D sketch with a 3D mechanical CAD system in the detail design phase is indispensable because inaccurate dimensions are inherent in a free-hand 2D sketch. For this reason, we present a new method to translate 3D data reconstructed from a free-hand 2D sketch into an editable form that reflects design intent so that the translated data can be directly used in 3D mechanical CAD systems. The feasibility of the proposed method has been demonstrated through experiments with prototype systems.

Proceedings ArticleDOI
14 Feb 2012
TL;DR: This work discusses the design of PhysicsBook, a prototype system that enables users to solve physics problems using a sketch-based interface and then animates any diagram used in solving the problem to show that the solution is correct.
Abstract: We present PhysicsBook, a prototype system that enables users to solve physics problems using a sketch-based interface and then animates any diagram used in solving the problem to show that the solution is correct. PhysicsBook recognizes the diagrams in the solution and infers relationships among diagram components through the recognition of mathematics and annotations such as arrows and dotted lines. For animation, PhysicsBook uses a customized physics engine that provides entry points for hand-written mathematics and diagrams. We discuss the design of PhysicsBook, including details of algorithms for sketch recognition, inference of user intent and creation of animations based on the mathematics written by a user. Specifically, we describe how the physics engine uses domain knowledge to perform data transformations in instances where it cannot use a given equation directly. This enables PhysicsBook to deal with domains of problems that are not directly related to classical mechanics. We provide examples of scenarios of how PhysicsBook could be used as part of an intelligent tutoring system and discuss the strengths and weaknesses of our current prototype. Lastly, we present the findings of a preliminary usability study with five participants.


Proceedings ArticleDOI
01 Jun 2012
TL;DR: A simple, natural system for gestural interaction between the user and computer for providing a dynamic user interface and uses image processing techniques for detection, segmentation, tracking and recognition of hand gestures for converting it to a meaningful command.
Abstract: With the escalating role of computers in educational system, human computer interaction, is becoming gradually more important part of it. The general believe is that with the progress in computing speed, communication technologies, and display techniques the existing HCI techniques may become a constraint in the effectual utilization of the existing information flow. The development of user interfaces influences the changes in the Human-Computer Interaction (HCI). Human hand gestures have been a mode of non verbal interaction widely used. The vocabulary of hand gesture communication has many variations. It ranges from simple action of using our finger to point at and using hands to move objects around for more complex expressions for the feelings and communicating with others. Also the hand gestures play a prominent role in teaching considering the explanations and exemplifications being highly dependent on hand gestures. Naturalistic and intuitiveness of the hand gesture has been an immense motivating aspect for the researchers in the field of Human Computer Interaction to put their efforts to research and develop the more promising means of interaction involving human and computers. The pursuance for the Human Computer Interaction research is moved by the central dogma of removing the complex and cumbersome interaction devices and replacing them with more obvious and expressive means of interaction which easily comes to the users with least cognitive burden like hand gestures. This paper designs a simple, natural system for gestural interaction between the user and computer for providing a dynamic user interface. The gesture recognition system uses image processing techniques for detection, segmentation, tracking and recognition of hand gestures for converting it to a meaningful command. This hand gesture recognition system has been proposed, designed and developed with the intensions to make it a substitute for mouse while making dynamic user interface between human and machine. Hence instead of making effort to develop a new vocabulary of hand gesture we have matched control instruction set of mouse to subset of most discriminating hand gestures, so that we get a robust interface. The interface being proposed here can be substantially applied towards different applications like image browser, games etc.

Journal ArticleDOI
01 Nov 2012
TL;DR: This paper proposes a multimodal communication method to estimate human behaviors based on the human detection using color image and 3-D distance information, and gesture recognition by the multilayered spiking neural network using the time series of human-hand positions.
Abstract: This paper proposes a multimodal communication method for human-friendly robot partners based on various types of sensors. First, we explain informationally structured space to extend the cognitive capabilities of robot partners based on environmental systems. Next, we discuss the suitable measurement range for recognition technologies of touch interface, voice recognition, human detection, gesture recognition, and others. Based on the suitable measurement ranges, we propose an integration method to estimate human behaviors based on the human detection using color image and 3-D distance information, and gesture recognition by the multilayered spiking neural network using the time series of human-hand positions. Furthermore, we propose a conversation system to realize the multimodal communication with a person. Finally, we show several experimental results of the proposed method, and discuss the future direction of this research.

Proceedings ArticleDOI
21 Mar 2012
TL;DR: In this paper, a novel facial sketch image or face-sketch recognition approach based on facial feature extraction is presented, where the facial features/components from training images are extracted, then ratios of length, width, and area etc are calculated and those are stored as feature vectors for individual images.
Abstract: This paper presents a novel facial sketch image or face-sketch recognition approach based on facial feature extraction. To recognize a face-sketch, we have concentrated on a set of geometric face features like eyes, nose, eyebrows, lips, etc and their length and width ratio because it is difficult to match photos and sketches because they belong to two different modalities. In this system, first the facial features/components from training images are extracted, then ratios of length, width, and area etc. are calculated and those are stored as feature vectors for individual images. After that the mean feature vectors are computed and subtracted from each feature vector for centering of the feature vectors. In the next phase, feature vector for the incoming probe face-sketch is also computed in similar fashion. Here, K-NN classifier is used to recognize probe face-sketch. It is experimentally verified that the proposed method is robust against faces are in a frontal pose, with normal lighting and neutral expression and have no occlusions. The experiment has been conducted with 80 male and female face images from different face databases. It has useful applications for both law enforcement and digital entertainment.

Proceedings ArticleDOI
Xiaoyu Wu1, Cheng Yang1, Youwen Wang1, Hui Li1, Shengmiao Xu1 
28 Oct 2012
TL;DR: Experimental results demonstrate that the gesture recognition algorithm is robust and effective, and the built interactive system with more intelligence improves the convenience and nature between human-computer interaction.
Abstract: In this paper, gesture recognition algorithm with kinect sensor is proposed the depth cue is used to locate the hand area Based on the histograms of oriented gradient (HOG) and adaboost learning methods, the static hand algorithm is designed to recognize the predefine gesture in the hand Area by tracking the hand trajectory by kinect, hmms is used to train and classify dynamic gesture an intelligent interactive system based on hand gesture recognition algorithm above is presented to choose and control different programs Experimental results demonstrate that our gesture recognition algorithm is robust and effective, and the built interactive system with more intelligence improves the convenience and nature between human-computer interaction

Proceedings ArticleDOI
26 Nov 2012
TL;DR: This paper presents and compares techniques that have been used to recognize the Arabic handwriting scripts in online recognition systems and attempts to recognize Arabic handwritten words, characters, digits or strokes.
Abstract: Online recognition of Arabic handwritten text has been an on-going research problem for many years. Generally, online text recognition field has been gaining more interest lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. However, different techniques have been used to build several online handwritten recognition systems for Arabic text, such as Neural Networks, Hidden Markov Model, Template Matching and others. Most of the researches on online text recognition have divided the recognition system into these three main phases which are preprocessing phase, feature extraction phase and recognition phase which considers as the most important phase and the heart of the whole system. This paper presents and compares techniques that have been used to recognize the Arabic handwriting scripts in online recognition systems. Those techniques attempt to recognize Arabic handwritten words, characters, digits or strokes. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed.

Proceedings ArticleDOI
21 Mar 2012
TL;DR: An overview of a current gesture recognition research is given by defining a specific number of terms then by presenting the current methods and examples of applications in gesture recognition.
Abstract: The visual interpretation of gestures provides the most natural and intuitive interaction. However its implementation remains very difficult. Consequently gesture recognition research tries to developing systems that are able to analyze, recognize and interpret the gestures captured by a camera. In this article we give an overview of a current research. Firstly by defining a specific number of terms then by presenting the current methods and examples of applications in gesture recognition.

Proceedings ArticleDOI
18 Sep 2012
TL;DR: A novel text recognition algorithm based on usage of fuzzy logic rules relying on statistical data of the analyzed font is suggested, enabling the recognition of distorted letters that may not be retrieved otherwise.
Abstract: Text recognition and retrieval is a well known problem. Automated optical character recognition (OCR) tools do not supply a complete solution and in most cases human inspection is required. In this paper the authors suggest a novel text recognition algorithm based on usage of fuzzy logic rules relying on statistical data of the analyzed font. The new approach combines letter statistics and correlation coefficients in a set of fuzzy based rules, enabling the recognition of distorted letters that may not be retrieved otherwise. The authors focused on Rashi fonts associated with commentaries of the Bible that are actually handwritten calligraphy.

Journal ArticleDOI
TL;DR: A system for gestural interaction between a user and a computer in dynamic environment using image processing techniques for detection, segmentation, tracking and recognition of hand gestures for converting it to a meaningful command.

Proceedings ArticleDOI
02 Mar 2012
TL;DR: This paper presents an intelligent hybrid features based face recognition method which combines the local and global approaches to produce a complete a robust and high success rate face recognition system.
Abstract: Face recognition is a biometric tool for authentication and verification having both research and practical relevance. A facial recognition based verification system can further be deemed a computer application for automatically identifying or verifying a person in a digital image. Varied and innovative face recognition systems have been developed thus far with widely accepted algorithms. The two common approaches employed for face recognition are analytic (local features based) and holistic (global features based) approaches with acceptable success rates. In this paper, we present an intelligent hybrid features based face recognition method which combines the local and global approaches to produce a complete a robust and high success rate face recognition system. The global features are computed using principal component analysis while the local features are ascertained configuring the central moment and Eigen vectors and the standard deviation of the eyes, nose and mouth segments of the human face as the decision support entities of the Generalized Feed Forward Artificial Neural Network (GFFANN). The proposed method's correct recognition rate is over 97%.

Journal ArticleDOI
16 Jan 2012
TL;DR: A novel approach for commanding mobile robots using a probabilistic multistroke sketch interface, where sketches are modeled as a variable duration hidden Markov model, where the distributions on the states and transitions are learned from training data.
Abstract: In this paper, a novel approach for commanding mobile robots using a probabilistic multistroke sketch interface is presented. Drawing from prior work in handwriting recognition, sketches are modeled as a variable duration hidden Markov model, where the distributions on the states and transitions are learned from training data. A forward search algorithm is used to find the most likely sketch given the observations on the strokes, interstrokes, and gestures. A heuristic is implemented to discourage breadth-first search behavior, and is shown to greatly reduce computation time while sacrificing little accuracy. To avoid recognition errors, the recognized sketch is displayed to the user for confirmation; a rejection prompts the algorithm to search for and display the next most likely sketch. Upon confirmation of the recognized sketch, the robot executes the appropriate behaviors. A set of experiments was conducted in which operators controlled a single mobile robot in an indoor search-and-identify mission. Operators performed two missions using the proposed sketch interface and two missions using a more conventional point-and-click interface. On average, missions conducted using sketch control were performed as well as those using the point-and-click interface, and results from user surveys indicate that more operators preferred using sketch control.