Recognition Rate Research Articles

Design and analysis of face recognition system based on VGGFace-16 with various classifiers

This research presents a face recognition system based on different classifiers that deal with various face positions. The proposed system involves the extraction of features through the VGG-Face-16 deep neural network, which only extracts essential features of input images, leading to an improved recognition step and enhanced algorithm efficiency, while the recognition involves the radial basis function in support vector machine (SVM) classifier and evaluate the performance of the system. Also, the system is designed and implemented later by using other classifiers; they are K-neareste2 neighbour (KNN) classifiers, logistic regression (LR), gradient boosting (XGBoost), decision tree classifier (DT) and Naive Bayes classifier (NB). The proposed algorithm was tested with the four face databases: AT&T, PINs Face, linear friction welding (LFW) and real database. The database was divided into two groups: One contains a percentage of images that are used for training and the second contains a percentage of images (remainder) which was used for testing. The results show that the classification by RBF in SVM has the highest recognition rate in the case of using small, medium and large databases; it was 100% in AT&T and Real database, while its efficiency appears to be lower when using large-size databases whereas it is 96% in PINs database and 60.1% in LFW database.

Tactile Transfer Learning and Object Recognition With a Multifingered Hand Using Morphology Specific Convolutional Neural Networks.

Multifingered robot hands can be extremely effective in physically exploring and recognizing objects, especially if they are extensively covered with distributed tactile sensors. Convolutional neural networks (CNNs) have been proven successful in processing high dimensional data, such as camera images, and are, therefore, very well suited to analyze distributed tactile information as well. However, a major challenge is to organize tactile inputs coming from different locations on the hand in a coherent structure that could leverage the computational properties of the CNN. Therefore, we introduce a morphology-specific CNN (MS-CNN), in which hierarchical convolutional layers are formed following the physical configuration of the tactile sensors on the robot. We equipped a four-fingered Allegro robot hand with several uSkin tactile sensors; overall, the hand is covered with 240 sensitive elements, each one measuring three-axis contact force. The MS-CNN layers process the tactile data hierarchically: at the level of small local clusters first, then each finger, and then the entire hand. We show experimentally that, after training, the robot hand can successfully recognize objects by a single touch, with a recognition rate of over 95%. Interestingly, the learned MS-CNN representation transfers well to novel tasks: by adding a limited amount of data about new objects, the network can recognize nine types of physical properties.

A geometric algebra-based approach for myoelectric pattern recognition control and faster prosthesis recalibration

ObjectiveAlthough many advancements have been made on myoelectric pattern-recognition, the control of poly-articulated upper-limb prostheses remains insufficiently robust. Electrode-shift, sweat or fatigue degrade the performance of classifiers over time, resulting in unfruitful device usage and frequent re-calibration. To tackle this issue, here we introduce two models − µP6 and µP8 − that combine Geometric Algebra with nearest-neighbor classification. We aim at reducing both the necessary training data and training time and, unlike most current state-of-the-art algorithms, we exploit an alternative geometric representation (and visualization) of the EMG signal as different polygons for different types of gestures, facilitating the explanation of the decision-making process to a layman. Moreover, we explore four abstention strategies to reduce the number of misclassifications. MethodsWe perform an offline analysis on two datasets, alongside two other standard models: nonlinear logistic regression (NLR) and linear discriminant analysis (LDA). ResultsEven with few training data, the proposed algorithms achieve high F1-scores (>0.95), significantly higher or non-significantly different from the values obtained with NLR and LDA, while maintaining relatively low abstentions rates and training times (<2 ms). ConclusionThe proposed algorithms allow to reduce the amount of training data and training times without compromising recognition rates. SignificanceThe proposed algorithms may contribute for a faster prosthesis re-calibration procedure while allowing to re-gain high recognition rates. Furthermore, the decision-making process is explainable and interpretable, potentially improving user trust and acceptance.

Face Detection under Hostile Circumstances using Innovative Robust Methods

Abstract: An attendance system is crucial for monitoring student presence in classes, with various methods available such as biometric, RFID card, face recognition, and traditional paper-based systems. Among these, face recognition stands out for its security and efficiency. This research focuses on enhancing the face recognition attendance system's accuracy by minimizing false positives through a confidence threshold based on the Euclidean distance metric. The Local Binary Pattern Histogram (LBPH) algorithm outperforms other distance-based methods like Eigenfaces and Fisherfaces due to its robustness against grayscale transformations. Face detection relies on Haar cascades for their robustness, while LBPH is employed for recognition. Leveraging LBPH for recognition tasks significantly improves the system’s ability to discern subtle variations in facial features, thereby enhancing overall accuracy. Moreover, the effectiveness of the system heavily relies on robust face detection techniques. Haar cascades are employed for this purpose, leveraging their proven reliability in detecting faces across diverse environmental conditions and camera angles. The system achieves a recognition rate of 77% for students and a false- positive rate of 28%. Notably, it effectively recognizes students even with variations like wearing glasses or facial hair. Face recognition for unknown individuals is approximately 60%,with false-positive rates of 14% and 30% with and without the threshold, respectively.

A Robust License Plate Detection System Using Smart Device

The license plate recognition (LPR) system is widely employed in various applications. However, most research studies have used a fixed camera rather than a moving one. This is because the location of the vehicle plate is nearly static and easily estimated, making the use of a static camera simple for locating and detecting the scanned license plate. Images obtained with a moving camera are highly complex due to frequent background changes. Additionally, a challenge with car plates in Malaysia is their non-standardized nature. Car owners are permitted to use any font type for their license plate number, rendering existing license plate recognition systems from other countries incapable of effectively detecting license plates on Malaysian car plates. A traditional LPR system typically requires a high-quality camera and a powerful computer for costly and bulky processing. Nowadays, many smartphones come equipped with powerful processors and cameras. Android smartphones include various libraries for modifying hardware configurations such as the camera. This paper presents a robust method for detecting Malaysia's license plate number using a convolutional neural network (CNN). The CNN model from the pre-training process is imported to the Android device and tested in real-time in an on-road driving environment, resulting in an average recognition rate of 89.37%. A comprehensive Character Recognition Analysis is also presented to demonstrate the accuracy of each character. However, there is still room for improvement in recognizing the character Q.

Enhancing OCR with line segmentation mask for container text recognition in container terminal

Optical Character Recognition (OCR) plays a pivotal role in enhancing the operational efficiency of container ports. However, challenges such as angle limitations and the complexity of container fonts in traditional OCR systems lead to tilted text and text adhesion, thereby reducing the recognition rate. Recognizing containers at a high speed is equally crucial for port operations. In this study, we address these challenges by introducing an Enhanced OCR (EOCR) system, incorporating Line Segmentation Mask (LSM)-based detection and Scanline-based recognition. LSM tackles the issue of text adhesion caused by traditional segmentation, while recognition based on scan lines accelerates efficiency. Additionally, we propose the arbitrary angle quadrilateral fitting algorithm targeting sloping quad areas in images taken at a container terminal. Experimental results on a dataset of container images from the Shanghai Port demonstrate superior performance compared to existing algorithms, achieving a recognition accuracy rate of up to 98.7%. Furthermore, an ablation study confirms that our EOCR significantly enhances recognition accuracy while ensuring real-time performance.

Neuromorphic Computing in Synthetic Antiferromagnets by Spin‐Orbit Torque Induced Magnetic‐Field‐Free Magnetization Switching

AbstractSynthetic antiferromagnet (SAF) with high thermal stability, ultra‐fast spin dynamics, and highly efficient spin‐orbit torque switching has great application potential in neuromorphic computing hardware. However, two challenges, the weakening of Hall signal in the remanent state and the need for a large auxiliary magnetic field for perpendicular magnetization switching, greatly limit the advantages of SAF in neuromorphic computing. In this work, both the enhanced anomalous Hall resistance and magnetic‐field‐free perpendicular magnetization switching are achieved by using oblique sputtering to fabricate the Pt/CoPt/Ru/CoTb SAF with strong interlayer exchange coupling and magnetic moment compensation. The fabricated SAF as synapse shows nearly linear, nonvolatile multistate plasticity, and as neuron exhibits a nonlinear sigmoid activation function, which are used to construct a fully connected neural network with a remarkable 97.0–98.1% recognition rate for the handwritten digits. Additionally, SAF serving as spike‐timing‐dependent plasticity synapse is used to construct an adaptive, unsupervised learning spiking neural network, and achieve an 87.0% accuracy in handwritten digit recognition. The findings exhibit the promise of SAFs as specialized hardware for high‐performance neuromorphic computing, offering high recognition rates and low power consumption.

Classification of Motor Imagery Tasks Derived from Unilateral Upper Limb based on a Weight-optimized Learning Model.

The accuracy of decoding fine motor imagery (MI) tasks remains relatively low due to the dense distribution of active areas in the cerebral cortex. To enhance the decoding of unilateral fine MI activity in the brain, a weight-optimized EEGNet model is introduced that recognizes six types of MI for the right upper limb, namely elbow flexion/extension, wrist pronation/supination and hand opening/grasping. The model is trained with augmented electroencephalography (EEG) data to learn deep features for MI classification. To address the sensitivity issue of the initial model weights to classification performance, a genetic algorithm (GA) is employed to determine the convolution kernel parameters for each layer of the EEGNet network, followed by optimization of the network weights through backpropagation. The algorithm's performance on the three joint classification is validated through experiment, achieving an average accuracy of 87.97%. The binary classification recognition rates for elbow joint, wrist joint, and hand joint are respectively 93.92%, 90.2%, and 94.64%. Thus, the product of the two-step accuracy value is obtained as the overall capability to distinguish the six types of MI, reaching an average accuracy of 81.74%. Compared to commonly used neural networks and traditional algorithms, the proposed method outperforms and significantly reduces the average error of different subjects. Overall, this algorithm effectively addresses the sensitivity of network parameters to initial weights, enhances algorithm robustness and improves the overall performance of MI task classification. Moreover, the method is applicable to other EEG classification tasks; for example, emotion and object recognition.

Script-style degrees

Abstract Previous work has shown that letters presented in special fonts with a high degree of script style have a poor recognition rate. We investigated whether there is a breaking point where this deficit sets in. In an experimental paradigm using a three-letter string partial report, 32 participants were presented with test stimuli of four new fonts with gradually increasing script style. The results of our investigation showed that each level of increasing script style resulted in significantly worse recognition. These findings demonstrate that for maximum letter recognition, the font style should be based on simple and familiar letter skeletons.

Category-Based Effect on False Memory of People with Down Syndrome.

Background: People with Down syndrome (DS) are deficient in verbal memory but relatively preserved in visuospatial perception. Verbal memories are related to semantic knowledge. Receptive ability is better than expressive ability in people with DS but still seriously lags behind their age-matched controls. This lag may result in the weak semantic integration of people with DS. Aims: This study aimed to examine the ability of semantic integration of people with DS by using false-memory tasks. Possible differences in the number of false memories induced by nouns and verbs were of focus. Methods and Procedures: Two phases were involved in the false-memory task. In the study phase, ten-word lists with semantically related associates were presented. In the recognition phase, judgments were to be made about whether the words presented had been heard before. Three types of words were tested: previously presented associates, semantically related lures, and semantically unrelated new words. Outcomes and Results: People with DS overall showed the lowest accuracy among groups in response to tested word types. In the processing of lures, people with DS were worse in recognition than MA controls. In processing unrelated words, people with DS responded least accurately to all types of words compared to control groups. In the processing of associates, people with DS showed similar recognition rates as the MA controls but were less accurate than the CA controls. No difference was observed between nouns and verbs in recognizing word types among groups, though faster responses to nouns than to verbs emerged in college students. Further analyses on topic-wised comparisons of errors across syntactic categories revealed differences in specific concepts among groups, suggesting people with DS were atypical in semantic organization. Conclusions and Implications: People with DS showed mixed patterns in semantic integration by false-memory tasks with delay to associates and deviance to lures together with unrelated words. People with DS showed distinct patterns in processing nouns and verbs while conducting topic-wise comparisons, suggesting that they formed false memories differently based on distinct syntactic categories. We concluded that people with DS develop a deviant semantic structure, hence showing problems in language and social cognition. Category-based rehabilitation is suggested to be implemented for people with DS to improve their semantic knowledge through lexical connections.

Hyperspectal imaging technology for phenotyping iron and boron deficiency in Brassica napus under greenhouse conditions

IntroductionThe micronutrient deficiency of iron and boron is a common issue affecting the growth of rapeseed (Brassica napus). In this study, a non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established.MethodsThe recognition accuracy was compared using the Fisher Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) recognition models. Recognition results showed that Multiple Scattering Correction (MSC) could be applied for the full band hyperspectral data processing, while the LDA models presented better performance on establishing the leaf iron and boron deficiency symptom recognition than the SVM models.ResultsThe recognition accuracy of the training set reached 96.67%, and the recognition rate of the prediction set could be 91.67%. To improve the model accuracy, the Competitive Adaptive Reweighted Sampling algorithm (CARS) was added to construct the MSC-CARS-LDA model. 33 featured wavelengths were selected via CARS. The recognition accuracy of the MSC-CARS-LDA training set was 100%, while the recognition accuracy of the MSC-CARS-LDA prediction set was 95.00%.DiscussionThis study indicates that, it is capable to identify the iron and boron deficiency in rapeseed using hyperspectral imaging technology.

Application of inertial navigation high precision positioning system based on SVM optimization

With the advancement of semiconductor technology, pedestrian navigation and positioning technology based on smartphones is becoming increasingly important in people's travel. However, precise positioning is challenging due to the use of inertial measurement units in low-cost smartphones and the complex motion states of pedestrians. To navigate and locate pedestrians in complex motion states, a method for converting between smartphone coordinate systems and navigation coordinate systems was studied and designed, and the errors of the built-in sensors of smartphones were analyzed and calibrated. In addition, support vector machines were used to optimize pedestrian trajectory prediction algorithms, and a pedestrian motion state recognition algorithm was designed based on this. To solve the classification problem of multiple human motion states, a multi classification model was constructed and adjacent gait correlation constraints were introduced to correct the classification results. Experiments indicated that the sum of squared errors for traditional algorithms estimating pedestrian trajectories was 0.92, whereas the optimized algorithms produced an improved sum of squared errors of 0.26. Consequently, the average sum of squared errors was reduced by 71.74 %, and the convergence speed increased by 55.56 %. The pedestrian trajectory prediction algorithm optimized by support vector machine could significantly lift the positioning and navigation efficiency, with a correct recognition rate of over 93 % and a position recognition accuracy of 78.8 % - 88.4 %. By optimizing recognition of the motion state of pedestrians, more accurate determination of their position and motion state can be achieved.

Low hysteresis, water retention, anti-freeze multifunctional hydrogel strain sensor for human-machine interfacing and real-time sign language translation.

Hydrogel strain sensors have received increasing attention due to their potential applications in human-machine interfaces and flexible electronics. However, they usually suffer from both mechanical and electrical hysteresis and poor water retention, which limit their practical applications. To address this challenge, a poly(acrylic acid-co-acrylamide) hydrogel crosslinked by silica nanoparticles is fabricated via photo polymerization and salting-out of hydrophilic ions for the strain sensor. The resulting hydrogel strain sensor possessed low electrical hysteresis (1.6%), low mechanical hysteresis (<7%), high cycle stability (>10 000 cycles), high durability, water retention and anti-freezing ability. Moreover, this strain sensor can be used as a wearable sensor for real-time control of robotic hands and hand gesture recognition. Finally, a sign language translation system has been demonstrated with the aid of machine learning, achieving recognition rates of over 98% for 15 different sign languages. This work offers a promising prospect for human-machine interfaces, smart wearable devices, and the Internet of Things.

Research on Fabric Defect Detection Algorithm Based on Improved YOLOv8n Algorithm

In the process of fabric production, various types of defects affect the quality of a fabric. However, due to the wide variety of fabric defects, the complexity of fabric textures, and the concealment of small target defects, current fabric defect detection algorithms suffer from issues such as having a slow detection speed, low detection accuracy, and a low recognition rate of small target defects. Therefore, developing an efficient and accurate fabric defect detection system has become an urgent problem that needs to be addressed in the textile industry. Addressing the aforementioned issues, this paper proposes an improved YOLOv8n-LAW algorithm based on the YOLOv8n algorithm. First, LSKNet attention mechanisms are added to both ends of the C2f module in the backbone network to provide a broader context area, enhancing the algorithm’s feature extraction capability. Next, the PAN-FPN structure of the backbone network is replaced by the AFPN structure, so that the different levels of features of the defects are closer to the semantic information in the progressive fusion. Finally, the CIoU loss is replaced with the WIoU v3 loss, allowing the model to dynamically adjust gradient gains based on the features of fabric defects, effectively focusing on distinguishing between defective and non-defective regions. The experimental results show that the improved YOLOv8n-LAW algorithm achieved an accuracy of 97.4% and a detection speed of 46 frames per second, while effectively increasing the recognition rate of small target defects.

Crater Triangle Matching Algorithm Based on Fused Geometric and Regional Features

Craters are regarded as significant navigation landmarks during the descent and landing process in small body exploration missions for their universality. Recognizing and matching craters is a crucial prerequisite for visual and LIDAR-based navigation tasks. Compared to traditional algorithms, deep learning-based crater detection algorithms can achieve a higher recognition rate. However, matching crater detection results under various image transformations still poses challenges. To address the problem, a composite feature-matching algorithm that combines geometric descriptors and region descriptors (extracting normalized region pixel gradient features as feature vectors) is proposed. First, the geometric configuration map is constructed based on the crater detection results. Then, geometric descriptors and region descriptors are established within each feature primitive of the map. Subsequently, taking the salience of geometric features into consideration, composite feature descriptors with scale, rotation, and illumination invariance are generated through fusion geometric and region descriptors. Finally, descriptor matching is accomplished by computing the relative distances between descriptors and adhering to the nearest neighbor principle. Experimental results show that the composite feature descriptor proposed in this paper has better matching performance than only using shape descriptors or region descriptors, and can achieve a more than 90% correct matching rate, which can provide technical support for the small body visual navigation task.

Recognition of facial emotion based on SOAR model.

Expressing emotions play a special role in daily communication, and one of the most essential methods in detecting emotions is to detect facial emotional states. Therefore, one of the crucial aspects of the natural human-machine interaction is the recognition of facial expressions and the creation of feedback, according to the perceived emotion. To implement each part of this model, two main steps have been introduced. The first step is reading the video and converting it to images and preprocessing on them. The next step is to use the combination of 3D convolutional neural network (3DCNN) and learning automata (LA) to classify and detect the rate of facial emotional recognition. The reason for choosing 3DCNN in our model is that no dimension is removed from the images, and considering the temporal information in dynamic images leads to more efficient and better classification. In addition, the training of the 3DCNN network in calculating the backpropagation error is adjusted by LA so that both the efficiency of the proposed model is increased, and the working memory part of the SOAR model can be implemented. Due to the importance of the topic, this article presents an efficient method for recognizing emotional states from facial images based on a mixed deep learning and cognitive model called SOAR. Among the objectives of the proposed model, it is possible to mention providing a model for learning the time order of frames in the movie and providing a model for better display of visual features, increasing the recognition rate. The accuracy of recognition rate of facial emotional states in the proposed model is 85.3%. To compare the effectiveness of the proposed model with other models, this model has been compared with competing models. By examining the results, we found that the proposed model has a better performance than other models.

Experimental Study on Using Synthetic Images as a Portion of Training Dataset for Object Recognition in Construction Site

The application of Artificial Intelligence (AI) across various industries necessitates the acquisition of relevant environmental data and the implementation of AI recognition learning based on this data. However, the data available in real-world environments are limited and difficult to obtain. Construction sites represent dynamic and hazardous environments with a significant workforce, making data acquisition challenging and labor-intensive. To address these issues, this experimental study explored the potential of generating synthetic data to overcome the challenges of obtaining data from hazardous construction sites. Additionally, this research investigated the feasibility of hybrid dataset in securing construction-site data by creating synthetic data for scaffolding, which has a high incidence of falls but low object recognition rates due to its linear object characteristics. We generated a dataset by superimposing scaffolding objects, from which the backgrounds were removed, onto various construction site background images. Using this dataset, we produced a hybrid dataset to assess the feasibility of synthetic data for construction sites and to evaluate improvements in object recognition performance. By finding the optimal composition ratio with real data and conducting model training, the highest accuracy was achieved at an 8:2 ratio, with a construction object recognition accuracy of 0.886. Therefore, this study aims to reduce the risk and labor associated with direct data collection at construction sites through a hybrid dataset, achieving data generation at a low cost and high efficiency. By generating synthetic data to find the optimal ratio and constructing a hybrid dataset, this research demonstrates the potential to address the problems of data scarcity and data quality on construction sites. The improvement in recognition accuracy of the construction safety management system is anticipated, suggesting that the creation of synthetic data for constructing a hybrid dataset can reduce construction safety-accident issues.

Specific emitter identification based on multiple sequence feature learning.

The specific emitter identification is widely used in electronic countermeasures, spectrum control, wireless network security and other civil and military fields. In response to the problems that the traditional specific emitter identification algorithm relies on a priori knowledge and has poor generalizability, and the existing specific emitter identification algorithm based on deep learning has poor feature selection and the adopted feature extraction network is not targeted, etc., the specific emitter identification algorithm based on multi-sequence feature learning is proposed. Firstly, multiple sequence features of the emitted signal of the communication radiation source are extracted, and these features are combined into multiple sequence features. Secondly, the multiple sequence fusion convolutional network is constructed to fuse and deeply extract the multiple sequence features and complete the classification of individual communication radiation sources through the classifier of neural network. The selected sequence features of this algorithm contain more and more essential RFF information, while the targeted design of the multi-sequence feature fusion learning network can effectively extract the essential RFF information. The results show that the algorithm can significantly improve the performance of SEI compared with the benchmark algorithm, with a recognition rate gain of about 17%.

Detection Model of Tea Disease Severity under Low Light Intensity Based on YOLOv8 and EnlightenGAN

In response to the challenge of low recognition rates for similar phenotypic symptoms of tea diseases in low-light environments and the difficulty in detecting small lesions, a novel adaptive method for tea disease severity detection is proposed. This method integrates an image enhancement algorithm based on an improved EnlightenGAN network and an enhanced version of YOLO v8. The approach involves first enhancing the EnlightenGAN network through non-paired training on low-light-intensity images of various tea diseases, guiding the generation of high-quality disease images. This step aims to expand the dataset and improve lesion characteristics and texture details in low-light conditions. Subsequently, the YOLO v8 network incorporates ResNet50 as its backbone, integrating channel and spatial attention modules to extract key features from disease feature maps effectively. The introduction of adaptive spatial feature fusion in the Neck part of the YOLOv8 module further enhances detection accuracy, particularly for small disease targets in complex backgrounds. Additionally, the model architecture is optimized by replacing traditional Conv blocks with ODConv blocks and introducing a new ODC2f block to reduce parameters, improve performance, and switch the loss function from CIOU to EIOU for a faster and more accurate recognition of small targets. Experimental results demonstrate that YOLOv8-ASFF achieves a tea disease detection accuracy of 87.47% and a mean average precision (mAP) of 95.26%. These results show a 2.47 percentage point improvement over YOLOv8, and a significant lead of 9.11, 9.55, and 7.08 percentage points over CornerNet, SSD, YOLOv5, and other models, respectively. The ability to swiftly and accurately detect tea diseases can offer robust theoretical support for assessing tea disease severity and managing tea growth. Moreover, its compatibility with edge computing devices and practical application in agriculture further enhance its value.

Air Handwriting using AI and ML

Air-writing refers to virtually writing linguistic characters through hand gestures in three dimensional space with six degrees of freedom. In this paper a generic video camera dependent convolutional neural network (CNN) based air-writing framework has been proposed. Gestures are performed using a marker of fixed color in front of a generic video camera followed by color based segmentation to identify the marker and track the trajectory of marker tip. A pre-trained CNN is then used to classify the gesture. The recognition accuracy is further improved using transfer learning with the newly acquired data. The performance of the system varies greatly on the illumi nation condition due to color based segmentation. In a less fluctuating illumination condition the system is able to recognize isolated unistroke numerals of multiple languages. The proposed framework achieved 97.7recognition rate in person inde pendent evaluation over English, Bengali and Devanagari numerals, respectively. Object tracking is considered as an important task within the field of Computer Vision. The invention of faster computers, availability of inexpensive and good quality video cameras and demands of automated video analysis has given popularity to object tracking techniques. Generally, video analysis procedure has three major steps: firstly, detecting of the object, secondly tracking its movement from frame to frame and lastly analysing the behaviour of that object. For object tracking, four different issues are taken into account; selection of suitable object representation, feature selection for tracking, object detection and object tracking. In real world, Object tracking algorithms are the primarily part of different applications such as: automatic surveillance, video indexing and vehicle navigation etc. The generated text can also be used for various purposes, such as sending messages, emails, etc. It will be a powerful means of communication for the deaf. It is an effective communication method that reduces mobile and laptop usage by eliminating the need to write. Key Words: Air Writing, Character Recognition, Object Detection, Real-Time Gesture Control System, Computer Vision , Hand tracking.