Pub Date : 2021-12-10DOI: 10.1109/SMART52563.2021.9676211
Anju Latha Nair S., R. K. Megalingam
Human Action Recognition is a challenging problem in the field of machine vision. It finds diverse application in a range of fields, whether it be in care of elderlies, or in sports, movies, interactive gaming and other areas. Videos from various sources need to be labelled so that an awareness on what exactly a person is doing can be recognized. Human Action recognition, in its primitive form can be thought of as a combination of three processes, feature extraction, classification on the basis of the extracted features and finally, recognition of actions. The action labels are a solution to many challenges like surveillance, video retrieval and health care problems. Processing the data online is of great help in automatic surveillance in hospitals, malls, sports galleries, homes with patients
{"title":"Human Action Recognition: A Review","authors":"Anju Latha Nair S., R. K. Megalingam","doi":"10.1109/SMART52563.2021.9676211","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676211","url":null,"abstract":"Human Action Recognition is a challenging problem in the field of machine vision. It finds diverse application in a range of fields, whether it be in care of elderlies, or in sports, movies, interactive gaming and other areas. Videos from various sources need to be labelled so that an awareness on what exactly a person is doing can be recognized. Human Action recognition, in its primitive form can be thought of as a combination of three processes, feature extraction, classification on the basis of the extracted features and finally, recognition of actions. The action labels are a solution to many challenges like surveillance, video retrieval and health care problems. Processing the data online is of great help in automatic surveillance in hospitals, malls, sports galleries, homes with patients","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131840634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/SMART52563.2021.9676308
R. Shukla, A. Sengar, Anurag Gupta, Arpit Jain, Abhilash Kumar, N. Vishnoi
Our sense of ourselves is inextricably linked to our looks. It’s required for everyday interactions, communication, and other routine duties. Face recognition algorithms that are both durable and perfect are required to construct fully automated systems that analyse the data contained in face photographs, and a variety of methodologies are currently being used. Partial facial occlusion is one of the most difficult challenges in face recognition. In real-world applications, face recognition algorithms can recognize faces hidden under masks, scarves, or sunglasses, hands on the face, things carried by a person, or external sources. The outcome, when compared to other existing algorithms, produces the best results. When utilising the suggested dataset, they provide high accuracy and a low loss function. With both trainable and non-trainable parameters, the suggested model performs admirably. The above-average accuracy of 80% indicates a strong performance in facial recognition. Face recognition from video and photos is extremely important.
{"title":"Face Recognition using Convolutional Neural Network in Machine Learning","authors":"R. Shukla, A. Sengar, Anurag Gupta, Arpit Jain, Abhilash Kumar, N. Vishnoi","doi":"10.1109/SMART52563.2021.9676308","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676308","url":null,"abstract":"Our sense of ourselves is inextricably linked to our looks. It’s required for everyday interactions, communication, and other routine duties. Face recognition algorithms that are both durable and perfect are required to construct fully automated systems that analyse the data contained in face photographs, and a variety of methodologies are currently being used. Partial facial occlusion is one of the most difficult challenges in face recognition. In real-world applications, face recognition algorithms can recognize faces hidden under masks, scarves, or sunglasses, hands on the face, things carried by a person, or external sources. The outcome, when compared to other existing algorithms, produces the best results. When utilising the suggested dataset, they provide high accuracy and a low loss function. With both trainable and non-trainable parameters, the suggested model performs admirably. The above-average accuracy of 80% indicates a strong performance in facial recognition. Face recognition from video and photos is extremely important.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132635021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/SMART52563.2021.9676283
Piyush Anand, Ajay Shankar Singh
Penetration testing (additionally referred as pen testing) is the exercise related to the checking out your PC framework, network or even internet based software aiming to discover vulnerabilities that the aggressor can likewise make the most. The main reason is constantly to peer how secure your very own machine can be or perhaps from the hacker’s point of view, exactly how confident your own machine is now. You have to be capable of look at almost all strategies which can be on the device, regardless of what computer or may be programming they work. This paper offers a top level view of equipment utilized in penetration testing.
{"title":"Penetration Testing Security Tools: A Comparison","authors":"Piyush Anand, Ajay Shankar Singh","doi":"10.1109/SMART52563.2021.9676283","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676283","url":null,"abstract":"Penetration testing (additionally referred as pen testing) is the exercise related to the checking out your PC framework, network or even internet based software aiming to discover vulnerabilities that the aggressor can likewise make the most. The main reason is constantly to peer how secure your very own machine can be or perhaps from the hacker’s point of view, exactly how confident your own machine is now. You have to be capable of look at almost all strategies which can be on the device, regardless of what computer or may be programming they work. This paper offers a top level view of equipment utilized in penetration testing.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132753665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/SMART52563.2021.9675304
G. Paliwal, U. Kurmi
Now a day’s lungs are very important body part due to indomitable cancer problem rice in patient. Lung cancer is the most harmful disease in human life. There are many patients, how safer from cancer now a day’s also suffer from covid related problems. In this survey paper discuss the different machine learning approach to disease lung cancer. for the detection of lung cancer. using machine learning first we collection the training data set for testing data set with the help of training of data set we have to learn system learn machine to disease the lung cancer. In this article discuss different machine learning approach for cancer detection using medical image processing (MIP) techniques. Image proceeding’s help to detection the cancer and machine learning technique prediction cell origination. Deep neural network is powerful tool for detection such type of deceases detection. In the review discuss the different techniques and it’s specification.
{"title":"A Comprehensive Analysis of Identifying Lung Cancer via Different Machine Learning Approach","authors":"G. Paliwal, U. Kurmi","doi":"10.1109/SMART52563.2021.9675304","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9675304","url":null,"abstract":"Now a day’s lungs are very important body part due to indomitable cancer problem rice in patient. Lung cancer is the most harmful disease in human life. There are many patients, how safer from cancer now a day’s also suffer from covid related problems. In this survey paper discuss the different machine learning approach to disease lung cancer. for the detection of lung cancer. using machine learning first we collection the training data set for testing data set with the help of training of data set we have to learn system learn machine to disease the lung cancer. In this article discuss different machine learning approach for cancer detection using medical image processing (MIP) techniques. Image proceeding’s help to detection the cancer and machine learning technique prediction cell origination. Deep neural network is powerful tool for detection such type of deceases detection. In the review discuss the different techniques and it’s specification.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127817273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/SMART52563.2021.9676225
Hradesh Kumar, D. Ather, Rani Astya
The intention of this paper zeroed in on improving learner performance forecast, considering their own and scholastic exhibition qualities. Because of the unbelievable development in ongoing innovation like online media, it might hinder the understudies from their real track, and this is one reason for the understudies to perform poor in scholarly exercises and it even prompts course nonconformists. Foreseeing understudies’ exhibition will make the student aware of think about their presentation and it allows as to improve their exhibition in future. The dataset utilized for the exploration purposes incorporates information about understudies’ exhibit from the scholastic and other homeroom exercises in the college during the course time., Educational information mining calculations is utilized to foresee the understudy execution which is a module in mechanized keen training frameworks.
{"title":"Predicting the Improvement in Academic Performance of the Student","authors":"Hradesh Kumar, D. Ather, Rani Astya","doi":"10.1109/SMART52563.2021.9676225","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676225","url":null,"abstract":"The intention of this paper zeroed in on improving learner performance forecast, considering their own and scholastic exhibition qualities. Because of the unbelievable development in ongoing innovation like online media, it might hinder the understudies from their real track, and this is one reason for the understudies to perform poor in scholarly exercises and it even prompts course nonconformists. Foreseeing understudies’ exhibition will make the student aware of think about their presentation and it allows as to improve their exhibition in future. The dataset utilized for the exploration purposes incorporates information about understudies’ exhibit from the scholastic and other homeroom exercises in the college during the course time., Educational information mining calculations is utilized to foresee the understudy execution which is a module in mechanized keen training frameworks.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127827642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/SMART52563.2021.9676270
Sheetal Agarwal, Srishty Jain, Amit Kumar
Docker is a free and open-source container engine created by Docker Inc and distributed under the Apache 2.0 licence in 2013. Containers have a unique place in computing history because of their role in infrastructure virtualization. Containers execute user space on top of the operating system kernel, unlike traditional hypervisor virtualization, which runs one or more independent computers virtually on physical hardware via an intermediate layer. Containers allow a user’s work environment to be divided into several instances. Docker containers are created using application images saved and maintained in Docker hub. The Containers/Apps view shows all of your containers and applications in real time. It lets you to communicate with containers and applications directly from your machine, as well as manage the lifetime of your applications. This paper focus on a user-friendly interface for inspecting, interacting with, and managing Docker objects, such as containers and Docker Compose-based applications.
{"title":"GUI Docker Implementation: Run Common Graphics User Applications Inside Docker Container","authors":"Sheetal Agarwal, Srishty Jain, Amit Kumar","doi":"10.1109/SMART52563.2021.9676270","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676270","url":null,"abstract":"Docker is a free and open-source container engine created by Docker Inc and distributed under the Apache 2.0 licence in 2013. Containers have a unique place in computing history because of their role in infrastructure virtualization. Containers execute user space on top of the operating system kernel, unlike traditional hypervisor virtualization, which runs one or more independent computers virtually on physical hardware via an intermediate layer. Containers allow a user’s work environment to be divided into several instances. Docker containers are created using application images saved and maintained in Docker hub. The Containers/Apps view shows all of your containers and applications in real time. It lets you to communicate with containers and applications directly from your machine, as well as manage the lifetime of your applications. This paper focus on a user-friendly interface for inspecting, interacting with, and managing Docker objects, such as containers and Docker Compose-based applications.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133774088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/SMART52563.2021.9676258
Jyoti Snehi, Manish Snehi, A. Bhandari, Vidhu Baggan, Rakesh Ahuja
Cloud computing is one of the most quickly developing computing advances in today’s IT environment. The cloud infrastructure links data and software from different geographically serving locations. In past few years, cloud computing has developed as a contemporary platform for extremely scalable and on-demand delivery. The most difficult challenge has been assuring the network’s reliability. In the cloud, shared pool IT services such as networks, servers, data, software, and utilities are subject to many types of intrusion attacks. Intrusion Detection Systems (IDS) are a form of security that helps to reduce the vulnerabilities of cloud environments. The purpose of this paper is to look into cloud-based intrusion detection systems as well as the techniques that support them. We examined the most recent cloud-based IDS solution implementations and proposed a Network Intrusion detection technology as a solution for cloud-based system security and protection.
{"title":"Introspecting Intrusion Detection Systems in Dealing with Security Concerns in Cloud Environment","authors":"Jyoti Snehi, Manish Snehi, A. Bhandari, Vidhu Baggan, Rakesh Ahuja","doi":"10.1109/SMART52563.2021.9676258","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676258","url":null,"abstract":"Cloud computing is one of the most quickly developing computing advances in today’s IT environment. The cloud infrastructure links data and software from different geographically serving locations. In past few years, cloud computing has developed as a contemporary platform for extremely scalable and on-demand delivery. The most difficult challenge has been assuring the network’s reliability. In the cloud, shared pool IT services such as networks, servers, data, software, and utilities are subject to many types of intrusion attacks. Intrusion Detection Systems (IDS) are a form of security that helps to reduce the vulnerabilities of cloud environments. The purpose of this paper is to look into cloud-based intrusion detection systems as well as the techniques that support them. We examined the most recent cloud-based IDS solution implementations and proposed a Network Intrusion detection technology as a solution for cloud-based system security and protection.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124124123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/SMART52563.2021.9676194
Vaibhav Jain, Ashendra Kumar Saxena, A. Senthil, A. Jain, Arpit Jain
Now a day’s our smart gadgets are not only devices but true friends of human-being. Social-Networking, one from them provides us a virtual home far from home, where everyone feels connected even from thousand miles is one of the brighter sides of new era. The dark side of this coin is equally the worst, as this also increases the vulnerability of young people to threatening situations online.This Paper is divided into three main tasks, as a very first task, we explored various forms of Cyber-Crime, reviewed Cyber-Bullying, its forms, methods, effects, and the available recent research to detect and prevent it. Secondly, for the experimental purpose, we have collected data of Twitter’s 35000+ tweets, prepared/wrangled that data to fed it to various smart machine learning algorithms, then applied five important ML algorithms to those tweets for classification and prediction into two main classes ‘offensive’ or ‘non-offensive’. Finally, a comparison has been done among those ML algorithms based on several performance metrics.
{"title":"Cyber-Bullying Detection in Social Media Platform using Machine Learning","authors":"Vaibhav Jain, Ashendra Kumar Saxena, A. Senthil, A. Jain, Arpit Jain","doi":"10.1109/SMART52563.2021.9676194","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676194","url":null,"abstract":"Now a day’s our smart gadgets are not only devices but true friends of human-being. Social-Networking, one from them provides us a virtual home far from home, where everyone feels connected even from thousand miles is one of the brighter sides of new era. The dark side of this coin is equally the worst, as this also increases the vulnerability of young people to threatening situations online.This Paper is divided into three main tasks, as a very first task, we explored various forms of Cyber-Crime, reviewed Cyber-Bullying, its forms, methods, effects, and the available recent research to detect and prevent it. Secondly, for the experimental purpose, we have collected data of Twitter’s 35000+ tweets, prepared/wrangled that data to fed it to various smart machine learning algorithms, then applied five important ML algorithms to those tweets for classification and prediction into two main classes ‘offensive’ or ‘non-offensive’. Finally, a comparison has been done among those ML algorithms based on several performance metrics.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124151041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The goal of this research study will be to research different machine learning algorithm via context oriented methodology with or without entropy for sub-pixel categorization utilizing Sentinel 2, multi-Spectral data extract reasonably accurate information for different land cover classes. To study the capabilities of Machine Learning Applications for Crop Identification and Use of temporal data information for crop planning in Machine Learning Algorithm this exploration will supportive to check Capability of Red Edge band to consolidate Crop phenology in crop recognizable proof. In this analysis work knowledge classification approach are going to be applied whereas getting ready land use and land covered map victimization for multi-spectral remote sensing knowledge sets (Sentinel-2/ Land sat). The data sets to be used in this research work will be fine spatial resolution data, to ensure classify approaches towards spatial data set and classification.
本研究的目的是研究不同的机器学习算法，通过上下文导向的方法，使用或不使用熵进行亚像素分类，利用Sentinel 2，多光谱数据提取不同土地覆盖类别的合理准确信息。为了研究机器学习应用于作物识别的能力，以及在机器学习算法中使用时间数据信息进行作物规划，该探索将有助于检查红边带在作物识别证明中巩固作物物候的能力。在本分析中，将应用工作知识分类方法，同时为多光谱遥感知识集(Sentinel-2/ land sat)准备土地利用和土地覆盖地图受害。本研究使用的数据集将是精细空间分辨率数据，以确保对空间数据集和分类的分类方法。
{"title":"Analysis Machine Learning Approach and Model on Hyper Spectral (Sentinel-2) Images for Land Cover Classification: Using SVM","authors":"Ranjana Sharma, Deepika Pantola, Shankar Dutt Kalony, Ritik Agarwal","doi":"10.1109/SMART52563.2021.9676331","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676331","url":null,"abstract":"The goal of this research study will be to research different machine learning algorithm via context oriented methodology with or without entropy for sub-pixel categorization utilizing Sentinel 2, multi-Spectral data extract reasonably accurate information for different land cover classes. To study the capabilities of Machine Learning Applications for Crop Identification and Use of temporal data information for crop planning in Machine Learning Algorithm this exploration will supportive to check Capability of Red Edge band to consolidate Crop phenology in crop recognizable proof. In this analysis work knowledge classification approach are going to be applied whereas getting ready land use and land covered map victimization for multi-spectral remote sensing knowledge sets (Sentinel-2/ Land sat). The data sets to be used in this research work will be fine spatial resolution data, to ensure classify approaches towards spatial data set and classification.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124316949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/SMART52563.2021.9675310
Pervez Shoaib Ilyasi, Gautam Gupta, M. S. Sai, K. Saatwik, B. S. Kumar, Dinesh Vij
The object recognition system based on deep learning has been applied in different domains, e.g. in the Intelligent transportation system, Autonomous driving system, etc. Along with object detection, in numerous scenes, text detection and recognition have conjointly brought abundant attention and analysis application.Real-time object detection and dimensioning as well as text recognition are important topics for many branches of the industry today.The projected system consists of object – text, detection and recognition module, and a dimension measuring module. This system offers an improved method of classifying objects and calculating their measures in real-time from video sequences. The proposed system uses OpenCV libraries, which comprise erosion algorithms, canny edge detection, dilation, and contour detection. To accomplish the task of the text recognition Tesseract OCR engine is employed.
{"title":"Object-Text Detection and Recognition System","authors":"Pervez Shoaib Ilyasi, Gautam Gupta, M. S. Sai, K. Saatwik, B. S. Kumar, Dinesh Vij","doi":"10.1109/SMART52563.2021.9675310","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9675310","url":null,"abstract":"The object recognition system based on deep learning has been applied in different domains, e.g. in the Intelligent transportation system, Autonomous driving system, etc. Along with object detection, in numerous scenes, text detection and recognition have conjointly brought abundant attention and analysis application.Real-time object detection and dimensioning as well as text recognition are important topics for many branches of the industry today.The projected system consists of object – text, detection and recognition module, and a dimension measuring module. This system offers an improved method of classifying objects and calculating their measures in real-time from video sequences. The proposed system uses OpenCV libraries, which comprise erosion algorithms, canny edge detection, dilation, and contour detection. To accomplish the task of the text recognition Tesseract OCR engine is employed.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114426891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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