malivinayak
This project harnesses the power of machine learning to accurately identify various denominations of Indian currency notes. It is built using TensorFlow.js and the Teachable Machine platform, allowing for easy integration and deployment in web applications.
Predicts denominations of Indian currency notes including 10, 20, 50, 100, 200, and 500 Rs notes.
Utilizes a trained model based on a dataset comprising over 450 images of each note denomination.
Provides real-time predictions with confidence levels.
Click on the "Choose File" button to upload an image containing an Indian currency note.
Click the "Predict" button to view the predicted denomination of the uploaded note.
The prediction result will be displayed along with the confidence level.
The model has been meticulously trained on a dataset comprising over 450 images of each note denomination, sourced from diverse sources to ensure comprehensive coverage and robust performance. Each image undergoes rigorous analysis to extract meaningful features, enabling the model to make informed predictions.
However, it's essential to acknowledge that while the model strives for accuracy, results may occasionally be subject to limitations inherent in any machine learning system. Factors such as image quality, lighting conditions, and variations in note condition may impact the model's predictions.
The image dataset used in training this model is drawn from Kaggle, a reputable platform known for its vast and high-quality datasets. This ensures that the model is trained on a diverse range of real-world examples, enhancing its ability to generalize and perform effectively in practical scenarios.