Currency Recognition Using Tensorflow.

Wed Mar 15, 2023

Recognizing currency from an image using Python can be achieved using various techniques such as image processing, computer vision, and machine learning. Here is a simple approach that uses machine learning with Python's OpenCV library.

  1. Dataset collection: Collect a dataset of currency notes images, including different currencies and denominations.
  2. Data preprocessing: Preprocess the images by resizing, normalizing, and converting them to grayscale.
  3. Feature extraction: Extract features from the images, such as texture, shape, and color using OpenCV. One popular method for feature extraction is the Local Binary Pattern (LBP) algorithm.
  4. Training the model: Split the dataset into training and testing sets, and train a machine learning model using the training set. One popular model for image classification is the Convolutional Neural Network (CNN).
  5. Model evaluation: Test the model's performance using the testing set, and evaluate its accuracy and other performance metrics.
  6. Prediction: Use the trained model to predict the currency denomination from a new input image.
Here is some sample code that demonstrates how to perform currency recognition using Python and OpenCV:

import cv2
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier

# Load the dataset
notes = ["1.png", "2.png", "5.png", "10.png", "20.png", "50.png", "100.png", "200.png"]
data = []
target = []
for note in notes:
img = cv2.imread(note, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (100, 100))

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)

# Train a K-Nearest Neighbors classifier
knn = KNeighborsClassifier(n_neighbors=3), y_train)

# Evaluate the model
y_pred = knn.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)

# Load a new image and predict its currency denomination
img = cv2.imread("test.png", cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (100, 100))
pred = knn.predict([img.flatten()])
print("Predicted denomination:", pred[0])

Note that this is just a simple example, and more sophisticated methods may be required for real-world applications. Additionally, it is important to ensure that the dataset is diverse and representative of the currencies and denominations that the model is intended to recognize.

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