The image dataset of Indian coins: A machine learning approach for Indian currency.

In an increasingly digital world, the significance of creating a Comprehensive Image Dataset of Contemporary Indian Coins (CIDCIC) cannot be overstated. This research presents a dataset comprising 6,672 images of 53 different classes of Indian coins, including denominations of 25 Paisa, 50 Paisa, 1 Rupee, 2 Rupee, 5 Rupee, 10 Rupee, and 20 Rupee. The images of coins with various shapes and sizes are taken from obverse and reverse sides in various environments and different backgrounds. The core significance of this dataset unfolds in its potential to offer invaluable assistance to visually impaired individuals as they navigate their daily financial transactions. The dataset is a significant contribution to the domains of computer vision, artificial intelligence, and machine learning, specifically addressing the challenges related to coin detection, recognition, and monetary system integrity. These technologies can empower visually impaired individuals to independently and accurately recognize and distinguish between various coin denominations, thereby enhancing their participation in the financial realm. The dataset addresses limitations in existing dataset of having limited size, and scope. It addresses the limitations associated to the limited number of coins and the lack of diversity in images, encompassing various angles, environments, backgrounds, and directions of coins. The dataset provides a broader and more up-to-date representation of contemporary Indian coins.

Face mask wearing image dataset: A comprehensive benchmark for image-based face mask detection models.

The Face Mask Wearing Image Dataset is a comprehensive collection of images aimed at facilitating research in the domain of face mask detection and classification. This dataset consists of 24,916 images, carefully categorized into two main folders: "Correct" and "Incorrect" representing instances of face masks being worn properly and improperly, respectively. Each folder is further divided into four subfolders, each denoting a specific type of face mask - Bandana, Cotton, N95, and Surgical. In the "Correct" folder, images depict individuals correctly wearing their respective face masks, while the "Incorrect" folder contains images of improper face mask usage. To capture variations in face mask application across different demographics, such as age and gender, each subfolder also includes three additional subfolders - Child, Male, and Female. The dataset's diverse content encompasses different face mask types, covering bandana-style, cloth, N95 respirators, and surgical masks, across various age groups and genders. This design ensures a comprehensive representation of real-world scenarios, enabling the evaluation of machine learning algorithms for face mask detection and classification. Researchers can leverage this dataset to develop and assess models that can accurately identify and distinguish between correct and incorrect face mask usage. By contributing to the advancement of face mask detection technologies, this dataset further supports public health initiatives and encourages proper mask-wearing behavior to mitigate the spread of infectious diseases, particularly during times of heightened health concerns such as the COVID-19 pandemic.

A comprehensive dataset of damaged banknotes in Indian currency (Rupees) for analysis and classification.

Detecting authentic and quality banknotes presents a significant challenge, particularly for individuals with low vision or visual impairments. Extensive research has been dedicated to achieving accurate banknote detection. It is crucial for clean banknotes to be readily detectable and accepted in daily transactions. However, existing Indian currency datasets suffer from limitations, including insufficient size, a lack of datasets on damaged/spoiled banknotes, and the unavailability of publicly accessible datasets featuring spoiled, torn, or altered banknotes. Recognizing the vital importance of a spoiled banknote dataset for the benefit of low vision and visually impaired individuals, we introduce a comprehensive dataset of spoiled banknotes comprising 5125 Indian currency notes. This dataset encompasses both old and new denominations of 10, 20, 50, and 100 Rupees, aiming to significantly enhance the accessibility and accuracy of banknote detection systems. By making this dataset openly accessible to the researchers, we aim to promote research and development of solutions for detection of spoiled banknote.

Dry fruit image dataset for machine learning applications.

Dry fruits are convenient and nutritious snacks that can provide numerous health benefits. They are packed with vitamins, minerals, and fibres, which can help improve overall health, lower cholesterol levels, and reduce the risk of heart disease. Due to their health benefits, dry fruits are an essential part of a healthy diet. In addition to health advantage, dry fruits have high commercial worth. The value of the global dry fruit market is estimated to be USD 6.2 billion in 2021 and USD 7.7 billion by 2028. The appearance of dry fruits is utilized for assessing their quality to a great extent, requiring neat, appropriately tagged, and high-quality images. Hence, this dataset is a valuable resource for the classification and recognition of dry fruits. With over 11500+ high-quality processed images representing 12 distinct classes, this dataset is a comprehensive collection of different varieties of dry fruits. The four dry fruits included in this dataset are Almonds, Cashew Nuts, Raisins, and Dried Figs (Anjeer), along with three subtypes of each. This makes it a total of 12 distinct classes of dry fruits, each with its unique features, shape, and size. The dataset will be useful for building machine learning models that can classify and recognize different types of dry fruits under different conditions, and can also be beneficial for dry fruit research, education, and medicinal purposes. Due to their nutritional value and health advantages, dry fruits have been consumed for a very long time. One of the best strategies to improve general health is to include dry fruits in the diet.

Dataset of Indian and Thai banknotes with annotations.

Multinational banknote detection in real time environment is the open research problem for the research community. Several studies have been conducted for providing solution for fast and accurate recognition of banknotes, detection of counterfeit banknotes, and identification of damaged banknotes. The State-of art techniques like machine learning (ML) and deep learning (DL) are dominating the traditional methods of digital image processing technique used for banknote classification. The success of the ML or DL projects heavily depends on size and comprehensiveness of dataset used. The available datasets have the following limitations:  1. The size of existing Indian dataset is insufficient to train ML or DL projects [1], [2].  2. The existing dataset fail to cover all denomination classes [1].  3. The existing dataset does not consists of latest denomination [3].  4. As per the literature survey there is no public open access dataset is available for Thai banknotes. To overcome all these limitations we have created a total 3000 image dataset of Indian and Thai banknotes which include 2000 images of Indian banknotes and 1000 images of Thai banknotes. Indian banknotes consist of old and new banknotes of 10, 20, 50, 100, 200, 500 and 2000 rupees and Thai banknotes consist of 20, 50, 100, 500 and 1000 Baht.