AUTOMATING SYMBOL RECOGNITION IN SPOT IT: ADVANCING AI-POWERED DETECTION

The "Spot It!" game, featuring 55 cards each with 8 unique symbols, presents a complex challenge of identifying a single matching symbol between any two cards. Addressing this challenge, machine learning has been employed to automate symbol recognition, enhancing gameplay and extending applications into areas like pattern recognition and visual search. Due to the scarcity of available datasets, a comprehensive collection of 57 distinct Spot It symbols was created, with each class consisting of 1,800 augmented images. These images were manipulated through techniques such as scaling, rotation, and resizing to represent various visual scenarios. Then developed a convolutional neural network (CNN) with five convolutional layers, batch normalization, and dropout layers, and employed the Adam optimizer to train model to accurately recognize these symbols. The robust dataset included over 102,600 images, each subject to extensive augmentation to improve the model's ability to generalize across different orientation and scaling conditions. 

The model was evaluated using 55 scanned "Spot It!" cards, where symbols were extracted and preprocessed for prediction. It achieved high accuracy in symbol identification, demonstrating significant resilience to common challenges such as rotations and scaling. This project illustrates the effective integration of data augmentation, deep learning, and computer vision techniques in tackling complex pattern recognition tasks, proving that artificial intelligence can significantly enhance traditional gaming experiences and create new opportunities in various fields. This project delves into the design, implementation, and testing of the CNN, providing a detailed analysis of its performance and highlighting its potential as a transformative tool in image recognition and categorization.