Chess position identification using pieces classification based on synthetic images generation and deep neural network fine-tuning - Campello, Rafael; Delgado, Afonso (Anais do Simpósio de Realidade Virtual e Aumentada (SVR), 2019)
Chess pieces recognition using computer vision is a problem generally approached in various ways, with different kinds of results and complexity. Deep learning is a state of the art approach to solve problems on image recognition although facing necessity of huge data sets. This paper discusses a method to identify synthetically generated chess images on Blender using its Python API via fine-tuning of VGG16 convolutional network obtaining close to 97% accuracy on piece classification. Possible applications include automated record of real chess games and real-time play between online players using real boards.