Currency Recognition Using Tensorflow.

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.

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))
    data.append(img.flatten())
    target.append(int(note.split(".")[0]))

# 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)
knn.fit(X_train, 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.