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.
| 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 |
- 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
picklemodule, enabling it to be saved and reloaded for future use without retraining.
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.
- 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.
- Dataset Compilation: Compiles the feature vectors (
X) and labels (y) from the processed images, ensuring accurate correspondence between features and targets.
- 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.
- Random Forest Classifier: Trains on the flattened image data, learning to recognize and classify each character based on its features.
- 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.
| 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 |