This paper presents a method for identifying 34 animal classes corresponding to the most conventional animals found in the domestic areas of Europe by using four types of Convolutional Neural Networks (CNNs), namely VGG-19, InceptionV3, ResNet-50, and MobileNetV2. We also built a system capable of classifying all these 34 animal classes from images as well as in real-time from videos or a webcam. Additionally, our system is capable to automatically generate two new datasets, one dataset containing textual information (i.e. animal class name, date and time interval when the animal was present in the frame) and one dataset containing images of the animal classes present and identified in videos or in front of a webcam. Our experimental results show a high overall test accuracy for all 4 proposed architectures (90.56% for VGG-19 model, 93.41% for InceptionV3 model, 93.49 for ResNet-50 model and 94.54% for MobileNetV2 model), proving that such systems enable an unobtrusive method for gathering a rich collection of information about the vast numbers of animal classes that are being identified such as providing insights about what animal classes are present at a given date and time in a certain area and how they look, resulting in valuable datasets especially for researchers in the area of ecology
This paper presents a method for detecting receipt fraud by implementing an Object Character Recognition (OCR) algorithm composed of Image Processing Techniques and Convolutional Neural Networks (CNNs). We implemented two CNN models into a smartphone application that gives customers the option to take pictures of products they intend to buy (also to crop their price tags) while present in a hypermarket/supermarket as well as of the paid receipt and succeeds to automatically identify and compare all prices (multiple digits including decimals) of the products seen at the shelf and all prices found in the paid receipt, received from the cashier. This application helps the customer detect a receipt fraud due to a computer or human error, in a cheap and convenient way. Experimental results show 99.96% overall test accuracy for the CNN responsible for identifying product prices and 99.35% overall test accuracy for the CNN responsible for identifying receipt prices.
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