As name characterizes understanding of a number plate accordingly, from past decades the use vehicles expanded rapidly, taking into account of this such a majority number of issues like overseeing and controlling trafficante keeping watch on autos and managing parking area zones to overcome this tag recognizer programming is required. The proposed work aims to detect speed of a moving vehicle through its license plate. It will fetch vehicle owner details with the help of CNN model. In this project the main focus is to detect a moving car whenever it crosses dynamic markings. It uses Tensor-flow with an SSD object detection model to detect cars and from the detection in each frame the license plate gets detected and each vehicle can be tracked across a video and can be checked if it crossed the markings made in program itself and hence speed of that vehicle can be calculated. The detected License plate will be forwarded to trained model where PyTesseract is used, which will convert image to text.

No abstract

For centuries, music has been an inseparable part of many human cultures. The rise of the hip-hop culture over the last 50 years has turned into a powerful movement, empowering people from various communities and making their voices heard. However, certain parts of hip-hop and rap music have started being associated with misogyny, substance abuse and violent behavior. This study aims to find a correlation between lyrics of hip-hop and rap songs that glorify such illicit behavior through their lyrics and the actual rate of criminal activity of individuals that are directly or indirectly influenced by hip-hop culture. This research employs NLP concepts to build a model that detects song lyrics that falls into any of the 3 categories-"Misogyny," "Substance Abuse" and "Violence." A comparative study is conducted by training multiple models including multinomial naïve Bayes, random forest and LSTM on a manually collected and labeled dataset consisting of rap song lyrics released between 1970 and 2020. The highest performing model (LSTM-87% accuracy) was subsequently used to detect objectionable lyrics in popular rap songs of the decade of 2010-2019. To obtain a correlation of these with the criminal activity of the target population, official data of criminal activity (2010-2019) of citizens aged 0-29 from the largest hip-hop influenced areas in the world are compiled. This dataset is analyzed and to obtain strong evidence of a correlation between objectionable rap song lyrics being promoted through song lyrics and the criminal tendencies of the youth that is primarily affected by it.

No abstract