Bangladeshi Currency (Coins & Notes) Recognition Dataset
Description
The Bangladeshi Currency (Coins & Notes) Recognition Dataset is a comprehensive collection of high-quality images of Bangladeshi coins and banknotes. It is designed to facilitate machine learning and computer vision applications for currency recognition, classification, and detection. This dataset is organized into various denominations of coins and notes, with each folder representing a specific currency denomination. Each folder contains 10,000 images, providing a total of 100,000 images in the dataset. The images have been resized to a uniform dimension of 256x256 pixels, ensuring consistency and enabling easy integration into machine learning workflows. The images are saved in JPEG format to optimize storage and speed for large-scale training tasks. Currency Denominations Included: 10 Poisha (Small denomination coin) 1 Poisha 1 Taka 25 Poisha 2 Taka 50 Poisha 5 Poisha 5 Taka Commemorative Coins Demonetized Notes Features: Image Size: All images have been resized to 256x256 pixels (Width x Height). Image Format: JPEG. Total Images: 100,000 (10,000 images per folder, one per denomination). Categories: Each folder corresponds to a unique denomination of currency. The folder names are aligned with the specific denominations such as 10_Poisha, 1_Taka, 5_Taka, etc. Objective: This dataset is ideal for training and evaluating models for the following tasks: Currency Classification: Identifying the denomination of a given image of a coin or banknote. Currency Recognition: Detecting and recognizing specific Bangladeshi coins and notes from real-world images. Coin and Note Detection: Identifying and classifying multiple coins and notes in a single image. Possible Use Cases: Currency detection systems: Automated systems in ATMs, vending machines, or cash counting machines that recognize Bangladeshi coins and banknotes. Banknote and Coin Classification: Machine learning models that classify various denominations of coins and notes for digital payment applications. Real-world Applications: Currency recognition for mobile apps, kiosks, or any system that needs to automatically recognize Bangladeshi currency. Research in Currency Image Recognition: Researchers working on currency recognition problems using computer vision techniques. Collected (https://www.bb.org.bd/currency) + own Note for Researchers Using the dataset This dataset was created by Shuvo Kumar Basak. If you use this dataset for your research or academic purposes, please ensure to cite this dataset appropriately. If you have published your research using this dataset, please share a link to your paper. Good Luck.
Files
Steps to reproduce
Download the Dataset: The dataset can be downloaded from Kaggle or any other hosting platform. Data Preprocessing: Once downloaded, you can use the images directly or apply additional preprocessing like image augmentation, normalization, etc., for training machine learning models. Model Training: You can use deep learning architectures such as Convolutional Neural Networks (CNNs) to classify currency notes and coins, or employ object detection techniques for currency recognition in real-world applications. Evaluate the Model: Use metrics like accuracy, precision, recall, and F1-score to evaluate the performance of your model on unseen test data from the dataset.