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Goal of the project is to recognize license plates without using deep learning methods.

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dariak153/Plate_recognition

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Character Recognition System

Overview

This project develops a character recognition system using a Random Forest Classifier, it classifies characters from various images, crucial for applications such as automatic license plate recognition. Below is an example of an image from the dataset used in this project:

Example Diagram

Result

Filename Identified License Plate
CIN20356.jpg CIN20356
CMG21FG.jpg CMG21FG
FSD23429.jpg FSD23429
PCT15PY.jpg PCT15PY
PGN141GR.jpg PGN141GR
PGN756EC.jpg PGN756EC
PKL8C63.jpg PKL8C63
PKRR788.jpg PKRR788L
PKS30W3.jpg PKS30W3
PO033AX.jpg POO33AX
PO096NT.jpg PO096NT
PO155KU.jpg PO155KU
PO2J735.jpg PO2J735
PO2W494.jpg PO2W494
PO522WJ.jpg PO522WJ
PO5T224.jpg PO5T224
PO6K534.jpg PO6K534
PO778SS.jpg PO778SS
POBTC81.jpg POBTC81
POZS221.jpg POZS221
PSE22800.jpg PSE22800
PSZ47620.jpg PSZ47620
PZ0460J.jpg PZ0460
PZ492AK.jpg PZ492AK
WSCUP62.jpg WSC7UG62
ZSL17729.jpg ZSL17729

Methods Used

  • Image Processing: The images are initially processed to improve the classifier's accuracy. This involves loading images from a dataset, converting them to grayscale to reduce complexity, applying a binary threshold to separate characters from the background, and dilating to emphasize features.
  • Data Preparation: Each processed image is resized to a uniform dimension (120x120 pixels) and flattened into a one-dimensional vector. This vectorization transforms the image data into a format suitable for machine learning models.
  • Model Training: Utilizing the RandomForestClassifier for training on processed image data.
  • Model Evaluation: After training, the model's effectiveness is evaluated through metrics such as precision, recall, and F1-score across a test dataset. These metrics help quantify the model’s ability to classify each character correctly.
  • Model Serialization: The fully trained model is serialized using Python’s pickle module, enabling it to be saved and reloaded for future use without retraining.

Files Overview

  • characters_dataset/: Contains labeled images of characters. Each label directory houses images representing a specific character, aiding in supervised learning.
  • characters_recognizer_rf.pkl: This file is a serialized version of the trained Random Forest model, ready for deployment in character recognition tasks.
  • templates/: directory contains a comprehensive template featuring all the characters used on license plates. This template is designed with a font style closely resembling that found on vehicle registration plates, making it ideal for training the character recognition system.

Data Preparation and Processing

  1. Image Loading and Processing: This crucial step involves several image manipulations to prepare data for training:
    • Grayscale Conversion: Converts the RGB image to grayscale to reduce computational load.
    • Thresholding: Applies a binary threshold to make the image binary, which is useful for isolating characters from backgrounds.
    • Dilation: Enhances features of the character in the binary image.
    • Resizing and Flattening: Standardizes the size of images and flattens them into arrays for machine learning processing.
  2. Dataset Compilation: Compiles the feature vectors (X) and labels (y) from the processed images, ensuring accurate correspondence between features and targets.

Model Training and Evaluation

  1. Training Data Split: The dataset is split into 80% for training and 20% for testing, maintaining a balance between learning and validation capabilities to prevent overfitting.
  2. Random Forest Classifier: Trains on the flattened image data, learning to recognize and classify each character based on its features.
  3. Performance Evaluation: The model’s performance is evaluated using classification metrics such as precision, recall, and F1-score, providing insights into its accuracy and effectiveness.

Classification Metrics Summary for License Plate Characters

precision recall f1-score support
0 1.00 1.00 1.00 38
1 1.00 1.00 1.00 31
2 1.00 1.00 1.00 29
3 1.00 1.00 1.00 35
4 1.00 1.00 1.00 35
5 1.00 1.00 1.00 33
6 1.00 1.00 1.00 29
7 1.00 1.00 1.00 30
8 1.00 1.00 1.00 29
9 1.00 1.00 1.00 36
A 1.00 1.00 1.00 36
B 1.00 1.00 1.00 34
C 1.00 1.00 1.00 26
D 1.00 1.00 1.00 22
E 1.00 1.00 1.00 25
F 1.00 1.00 1.00 34
G 1.00 1.00 1.00 28
H 1.00 1.00 1.00 32
I 1.00 1.00 1.00 23
J 1.00 1.00 1.00 26
K 1.00 1.00 1.00 27
L 1.00 1.00 1.00 25
M 1.00 1.00 1.00 25
N 1.00 1.00 1.00 31
O 1.00 1.00 1.00 31
P 1.00 1.00 1.00 25
R 1.00 1.00 1.00 29
S 1.00 1.00 1.00 30
T 1.00 1.00 1.00 42
U 1.00 1.00 1.00 30
V 1.00 1.00 1.00 38
W 1.00 1.00 1.00 30
X 1.00 1.00 1.00 31
Y 1.00 1.00 1.00 25
Z 1.00 1.00 1.00 28
precision recall f1-score support
accuracy 1.00 1058
macro avg 1.00 1.00 1.00 1058
weighted avg 1.00 1.00 1.00 1058

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Goal of the project is to recognize license plates without using deep learning methods.

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