Bangladeshi Currency (Coins & Notes) Recognition Dataset

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.孟加拉国货币（硬币与纸币）识别数据集（Bangladeshi Currency (Coins & Notes) Recognition Dataset）是一套收录高质量孟加拉国硬币与纸币图像的综合性数据集，旨在为货币识别、分类与检测相关的机器学习(Machine Learning)及计算机视觉(Computer Vision)应用提供支撑。 本数据集按货币面额对硬币与纸币进行分类组织，每个文件夹对应一种特定的货币面额。每个文件夹包含10000张图像，全数据集总图像量达100000张。 所有图像均被统一调整至256×256像素的尺寸，以保证数据一致性，便于无缝集成至机器学习工作流中。图像采用JPEG格式存储，可优化大规模训练任务的存储效率与运行速度。 包含的货币面额如下： 10波伊沙（小额硬币）、1波伊沙、1塔卡、25波伊沙、2塔卡、50波伊沙、5波伊沙、5塔卡、纪念硬币、作废纸币 数据集特性： 图像尺寸：所有图像均调整为256×256像素（宽×高）。 图像格式：JPEG。 总图像量：100000张（每个面额对应文件夹含10000张图像）。 类别划分：每个文件夹对应唯一的货币面额，文件夹名称与具体面额保持一致，例如10_Poisha、1_Taka、5_Taka等。 数据集目标： 本数据集适用于训练与评估以下任务的模型： 货币分类：识别给定硬币或纸币图像的面额。 货币识别：从真实场景图像中检测并识别特定的孟加拉国硬币与纸币。 硬币与纸币检测：在单张图像中识别并分类多张硬币与纸币。 潜在应用场景： 货币检测系统：应用于自动柜员机、自动售货机或点钞机的自动化系统，实现孟加拉国硬币与纸币的自动识别。 纸币与硬币分类：为数字支付应用中的各类货币面额分类机器学习模型提供训练支撑。 实际落地应用：为移动应用、自助服务终端或其他需要自动识别孟加拉国货币的系统提供货币识别能力。 货币图像识别研究：为采用计算机视觉技术开展货币识别相关研究的学者提供数据支持。 数据采集来源：https://www.bb.org.bd/currency 及自有资源 数据集使用须知（供研究者参考）： 本数据集由Shuvo Kumar Basak创建。若您将本数据集用于研究或学术用途，请务必对该数据集进行恰当引用。若您基于本数据集发表研究成果，请分享您的论文链接。祝研究顺利。