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7. Multilanguage Number Plate Detection using Convolutional Neural Networks

Author

Gupta, Jatin, Saini, Vandana, and Garg, Kamaldeep

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Object Detection is a popular field of research for recent technologies. In recent years, profound learning performance attracts the researchers to use it in many applications. Number plate (NP) detection and classification is analyzed over decades however, it needs approaches which are more precise and state, language and design independent since cars are now moving from state to another easily. In this paperwe suggest a new strategy to detect NP and comprehend the nation, language and layout of NPs. YOLOv2 sensor with ResNet attribute extractor heart is proposed for NP detection and a brand new convolutional neural network architecture is suggested to classify NPs. The detector achieves average precision of 99.57% and country, language and layout classification precision of 99.33%. The results outperforms the majority of the previous works and can move the area forward toward international NP detection and recognition.

Published
2020

16. Contributors

Author

Anadón, Arturo, primary, Anantharam, Vellareddy, additional, Archibong, Anthony E., additional, Ares, Irma, additional, Aulbach, Adam D., additional, Awasthee, Nikee, additional, Banerjee, Aryamitra, additional, Banks, Leah D., additional, Barile, Frank A., additional, Beedanagari, Sudheer R., additional, Belantis, Charalampos, additional, Bergamaschi, Enrico, additional, Bhandari, Sadikshya, additional, Bhatia, Sneha P., additional, Bischoff, Karyn, additional, Borts, David J., additional, Brehm, Emily, additional, Chandra Gupta, Subash, additional, Chatterjee, Saurabh, additional, Chiang, Catheryne, additional, Chintalapati, Anirudh J., additional, Cohn, P., additional, Coppock, Robert W., additional, Costa, Lucio G., additional, Damodaran, Tirupapuliyur V., additional, D'Souza, Clinton, additional, Dettbarn, Wolf-D., additional, Devlin, Amy A., additional, Doss, Robin B., additional, Dwivedi, Shiwangi, additional, Dziwenka, Margitta M., additional, Estévez, Jorge, additional, Fabricant, Daniel S., additional, Fan, A.M., additional, Fitsanakis, Vanessa A., additional, Flaskos, John, additional, Flaws, Jodi A., additional, Flora, Swaran J.S., additional, Ford, Sue M., additional, Fortin, Jessica S., additional, Fragou, Domniki, additional, Gad, Shayne C., additional, Galateanu, Bianca, additional, Gardner, Dale R., additional, Georgiadis, George, additional, Gil, Fernando, additional, Goel, Saryu, additional, Gulumian, Mary, additional, Gupta, P.K., additional, Gupta, Ramesh C., additional, Gupta, Rekha K., additional, Gwaltney-Brant, Sharon, additional, Hargreaves, Alan J., additional, Harris, Kelly L., additional, Harris, Kenneth J., additional, Hatfield, Holly E., additional, Hayes, Wallace A., additional, Heretis, Ioannis, additional, Hernández, Antonio F., additional, Hilmas, Corey J., additional, Hood, Darryl B., additional, Huuskonen, Pasi, additional, Jacobson, Stewart B., additional, James-Yi, Sandra A., additional, Jin, Huajun, additional, Kanno, Jun, additional, Kanthasamy, Arthi, additional, Kanthasamy, Anumantha G., additional, Kaore, Shilpa N., additional, Kaore, Navinchandra M., additional, Kaphalia, Bhupendra S., additional, Karttunen, Vesa, additional, Kaur, Gurjot, additional, Kaur, Ravneet, additional, Kodavanti, Prasada Rao S., additional, Kodavanti, Urmila P., additional, Kontadakis, George A., additional, Krishna, Gopala, additional, Krishna, Priya A., additional, Krishna, Kavya A., additional, Kummu, Maria, additional, Kymionis, George D., additional, Lall, Rajiv, additional, Lin, P., additional, Loganathan, Bommanna G., additional, Loikkanen, Jarkko, additional, Lotti, Marcello, additional, Lynes, Michael A., additional, Mahadevan, Brinda, additional, Malik, Jitendra K., additional, Mamoulakis, Charalampos, additional, Mantey, Jane A., additional, Martínez-Larrañaga, María Rosa, additional, Martínez, María Aránzazu, additional, Mavridis, Charalampos, additional, McClellan, Roger O., additional, Meador, Vincent P., additional, Mikkelsen, Lars Friis, additional, Milatovic, Dejan, additional, Miller Mukherjee, Ida R., additional, Mukherjee, Anupama, additional, Multani, Pushpinder Kaur, additional, Myllynen, Päivi, additional, Myöhänen, Kirsi, additional, Negga, Rekek, additional, Negrei, Carolina, additional, Novilla, Meliton N., additional, Padilla, Stephanie, additional, Palmeira, Carlos M., additional, Panter, Kip E., additional, Pasanen, Markku, additional, Patrick, Daniel J., additional, Pavanello, Sofia, additional, Pedersen, Henrik Duelund, additional, Pelkonen, Olavi, additional, Pellizzon, Michael A., additional, Pitt, Jason, additional, Plaka, Argyro, additional, Ramesh, Aramandla, additional, Rattan, Saniya, additional, Repo, Jenni, additional, Ricci, Matthew R., additional, Rolo, Anabela P., additional, Sachana, Magdalini, additional, Sahlman, Heidi, additional, Saini, Nitin, additional, Saini, Vandana, additional, Savolainen, Kai, additional, Seth, Ratanesh Kumar, additional, Sharma, Abha, additional, Sharma, Anurag, additional, Sieppi, Elina, additional, Silva, Rui, additional, Sinha, Anita, additional, Skamagkas, Iordanis, additional, Snow, Samantha J., additional, Sogorb, Miguel A., additional, Srivastava, Ajay, additional, Stice, Szabina A., additional, Storvik, Markus, additional, Szabo, David T., additional, Teodoro, João S., additional, Tsatsakis, Aristidis M., additional, Tsiaoussis, John, additional, Vähäkangas, Kirsi, additional, Verma, Sumit Singh, additional, Vilanova, Eugenio, additional, Vulimiri, Suryanarayana V., additional, Warner, Genoa R., additional, Welch, Kevin D., additional, Wilson-Frank, Christina, additional, You, S.H., additional, Zaja-Milatovic, Snjezana, additional, Zisis, Ioannis E., additional, and Zoltani, Csaba K., additional

Published
2019
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22. STUDY OF CLINICAL PROFILE, RISK-FACTORS AND MANAGEMENT OF POST PARTUM HAEMORRHAGE IN A TERTIARY CARE CENTRE

Author

Saini, Vandana, Pratixaben Chaudhari, Heenal Vaghela, and Rathod, Kuldeep

Subjects
Postpartum Haemorrhage Atonic Uterus Obstetric Hysterectomy
Abstract

Introduction:Post-partum Haemorrhage is commonly defined as cumulative blood loss of greater than or equal to 1000ml or blood loss accompanied by signs or symptoms of hypovolemia within 24 hrs after birth process. PPH accounts for 35% of maternal deaths worldwide. Objective:To study clinical profile, risk factors and management of Postpartum Haemorrhage in tertiary care centre. Materials and Methods: A Prospective observational study was conducted collecting data from medical records of consenting subjects who had Postpartum Haemorrhage in a tertiary care centre over a period between September 2019 to September 2021 after ethics approval. Results:In our study, PPH was seen higher(52.08%) in study subjects between age group of 25-30 years and in Primipara subjects. Atonic uterus was the major cause of PPH identified in 59.3% of subjects where 16.67% of subjects were effectively managed medically by Oxytocin, Misoprostol and Methergine alone. Remaining subjects required cervical/vaginal tear repair, utero-vaginal packing, uterine artery ligation and Obstetric hysterectomy. Secondary PPH was seen in 5.2% of subjects where the etiology identified was Retained products. Conclusion:As obstetric care improves, the incidence of postpartum haemorrhage decreases and maternal deaths from it drastically reduced. Hence, a multicentric approach has to be made to deal with these complications. &nbsp

Published
2022
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35. WEED IDENTIFICATION USING MACHINE LEARNING MODELS.

Author

SAINI, VANDANA, SHALINI, and SHARMA, KRISHAN KUMAR

Subjects
MACHINE learning, SUPPORT vector machines, RANDOM forest algorithms, DECISION trees, SEEDLINGS
Abstract

Image classification is a complex process and an important direction in the field of image processing. Image classification methods require learning and training stages. Using machine learning classification models in image classification gives better results. Decision Tree, Random Forest, Gradient Boosting, Bagging Classifier, Multi-Layer Perceptron (MLP) Classifier, and Support Vector Machine (SVM) are different machine-learning classification models. The goal of this paper is to analyze the machine learning classification models. These models classify 12 kinds of plant seedlings, of which 3 are crop seedlings and 9 are weed seedlings. This paper suggests that, when using a V2 Plant Seedlings dataset, the accuracy of SVM is 0.71 and the accuracy of other models is less compared to SVM. The experimental results in this paper show that the machine learning model SVM has a better solution effect and higher recognition accuracy. This paper focuses on model building, training, and assessing the quality of the model by generating a confusion matrix and a classification report. [ABSTRACT FROM AUTHOR]

Published
2022
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46. Priority - Discipline Queue Model for Passport Office System.

Author

Saini, Vandana and Gupta, Deepak

Subjects
OFFICE equipment & supplies, POISSON processes, POISSON distribution, PASSPORTS, HOSPITAL administration
Abstract

This paper presents a mathematical queue model for passport office system. It provides a more realistic description of queue model for passport office system on priority basis. Now a days almost in every system priority is given in some cases for example medical cases, to some officials and customers\applicants having some emergency. This model consists two rows, one for high priority and other for low priority customers. It is assumed that the arrival and service process follow the Poisson distribution. It is supposed that the service will be completed when the customer\applicant pass through all the service channels. This model can also be applicable in many real life situations like hospital management, in market, in offices etc. [ABSTRACT FROM AUTHOR]

Published
2021

47. Analysis of a Complex Feedback Queue Network.

Author

Saini, Vandana and Gupta, Deepak

Subjects
OFFICES, TELECOMMUNICATION systems, PSYCHOLOGICAL feedback, CONSUMERS
Abstract

The aim of the paper is to discuss a system of complex feedback queue model containing three subsystems; one is comprised with two biserial service channels and other with two parallel service channels. Both the subsystems are commonly connected with a central service channel. The customers have the facility to revisit any of the service channels at most once with different probability of leaving the server and moving from one server to another server. Mean queue length is derived in steady state condition. Particular values are given to the variables to check the validity of the model. The model is applicable to many real life situations such as in shopping malls, offices, communication networks etc. [ABSTRACT FROM AUTHOR]

Published
2021

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