Convolutional Neural Network Method Implementation for License Plate Recognition in Android

Astawa, I Nyoman Gede Arya; Caturbawa, I Gusti Ngurah Bagus; Rudiastari, Elina; Radhitya, Made Leo; Hariyanti, Ni Kadek Dessy

doi:10.1109/eiconcit.2018.8878658

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A wide range of methods of character segmentation exists. Recognition has been performed by classifying the segmented characters and inputting them into a CNN model for training [ 48 , 49 ], but the character segmentation is susceptible to image-related factors that cause difficulties [ 50 , 51 ]. The direct recognition of characters without segmentation has been developed [ 52 , 53 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Chou

Liu

2021

Sensors

View full text Add to dashboard Cite

Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Chou

Liu

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Some researchers use a different approach, modifying the balancing characteristic vectors and grouping them to find brief variations in stock prices when balance sheets are released. Time series data clustering is still in its infancy, especially for multivariate data series, but static data clustering has been thoroughly studied [2]. Nonetheless, several studies have demonstrated that time series clustering plays a significant role in trend and price prediction.…”

Section: Introductionmentioning

confidence: 99%

The Application of Neural Network Models to Explain the Relationship Between Stock Value, Returns Value, and Information on Prices

Dessy Hariyanti,

Iwan Henri Kusnadi,

Rezky Nurbakti

et al. 2024

jidt

View full text Add to dashboard Cite

This study aims to identify features for stock market value predictions based on time series grouping. This research uses various data sets for different activities. We have the balance sheet data set, which includes a series of quarterly balance sheets. Next, there is the ratio data set. The clustering data set consists of a series of daily prices. There are two data sets for testing activities: pilot forecasts and investments with daily data, projections, and investments. To speed up the development and exploration of different methods, testing begins with a subset of the data, with additional shares of witnesses. This research uses ARIMA and artificial neural networks to predict stock prices. Predicted results provide an investment strategy that compares the results obtained with what happens, resulting in generally positive profits. The capacity of artificial neural networks to manage non-linearities in financial data explains this. Second, many experiments demonstrate that not all data can be utilized using predicting techniques. These results indicate that improved estimations do not always result from more significant data. Forecast accuracy and performance on the test set will likely be negatively impacted by noise and competing signals introduced by data from unrelated stocks. Put another way, by including unrelated data, the information about the action supplied by the group of which it is a member is obscured. As such, the forecasts more closely align with the market's average behavior than with the performance of a specific stock.

show abstract

A Systematic Literature Review: The Effectiveness of ANPR for Electronic Law Traffic Enforcement

Setiadi,

Stefani,

Gunawan

2023

2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS)

View full text Add to dashboard Cite

Convolutional Neural Network Method Implementation for License Plate Recognition in Android

Cited by 3 publications

References 8 publications

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

The Application of Neural Network Models to Explain the Relationship Between Stock Value, Returns Value, and Information on Prices

A Systematic Literature Review: The Effectiveness of ANPR for Electronic Law Traffic Enforcement

Contact Info

Product

Resources

About