Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/josualimbu/npr-with-ocr

Number Plate Recognition with Optical Character Recognition, I made to extend its functionality from mere object detection to object detection and optical character recognition, specifically tailored for the "plate" class only. Additionally, I have trained the dataset and added over 7000 more datasets for this purpose.
https://github.com/josualimbu/npr-with-ocr

Last synced: 19 days ago
JSON representation

Number Plate Recognition with Optical Character Recognition, I made to extend its functionality from mere object detection to object detection and optical character recognition, specifically tailored for the "plate" class only. Additionally, I have trained the dataset and added over 7000 more datasets for this purpose.

Awesome Lists containing this project

README 

        
## Number Plate Recognition with Optical Character Recognition



This code is not open source, it is for my personal portfolio. If you want open source, please visit [simple-npr](https://github.com/JosuaLimbu/npr-tesseract)



Number Plate Recognition (NPR), also known as Automatic Number Plate Recognition (ANPR) or License Plate Recognition (LPR), is a technology used to automatically detect and recognize vehicle license plates. The system uses an optical camera and character recognition (OCR) software to capture images of license plates from passing vehicles, then analyzes the images to recognize and extract the text contained within. In some cases, a Convolutional Neural Network (CNN), a type of deep learning algorithm designed for image recognition, is used to improve the accuracy of character recognition. CNNs are capable of automatically learning features from images, allowing them to recognize distorted or partially obscured characters on license plates. Upon successful reading using OCR or CNN, the extracted license plate data is stored in a MySQL database. This code is integrated into the [U-Park](https://github.com/JosuaLimbu/u-park/) web system using PHP.



### Install



**Install on Windows**



Install [Git Bash](https://git-scm.com/downloads), [Python](https://www.python.org/downloads/) and [Tesseract](https://sourceforge.net/projects/tesseract-ocr.mirror/) first



After that, open git bash and follow these commands



```bash

$ git clone https://github.com/JosuaLimbu/npr-tesseract.git

$ cd npr-with-ocr

$ pip install -r requirements.txt

$ python3 detect3.py

```



## Performance Evaluation



**Precison-Recall Value**



![Precison-Recall Value](img/PR_curve.png)



The training results demonstrated high performance, with an average precision-recall value reaching 0.953 [email protected] in relation to the recall, The precision-recall curve is shown, indicating the model's performance in accurately detecting objects.



**Recall-Confidence Value**



![Recall-Confidence Value](img/R_curve.png)



The highest average recall reaches 0.97 at a confidence level of 0.00



**The Results of the Training Data Evaluation**



![Result](img/results.png)



All test results are stored in the directory runs/train/exp. The tests were conducted using the parameters batch = 16 and epoch = 50



**F1-Confidence Value**



![F1-Confidence Value](img/F1_curve.png)



The peak F1 score reaches 0.93 at a confidence level of 0.516



**Precision-Confidence Value**



![Precision-Confidence Value](img/P_curve.png)



The Precision score reaches 1.00 at a confidence level of 0.966



**Confusion Matrix**



![Confusion Matrix](img/confusion_matrix.png)



A confusion matrix is a valuable tool for assessing the effectiveness of a classification model, displaying the counts of correct and incorrect predictions for each category. It provides insights into true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN), which are essential for calculating additional metrics like accuracy, precision, and recall. Analyzing the confusion matrix helps researchers understand the model's strengths and weaknesses in classifying different categories, enabling more targeted improvements in future iterations.



The end result will be like this.



![Result](img/Result.jpg)



And the display results if integrated into a [U-Park](https://github.com/JosuaLimbu/u-park/) Web Application like this



![webapp](img/webapp.png)