Nowadays there are lots of emerging use cases and fields to artificial intelligence/machine learning technologies. One of several relevant fields is that of image recognition model training, which itself has several potential applications already in development. This project studies this field through a specific case study: a machine learning model built to recognise and differentiate between chess piece images. For models to accurately discern what they are being presented with it is pivotal to meticulously assemble and refine the data they receive. In this project, based on a previously existing dataset of chess board images, we delve with the acquisition of curated chessboard images, each representing a spectrum of game states and scenarios. This is done in two ways: firstly, by capturing images from physical chess boards during in-person tournaments, and secondly by recreating digital versions of real professional chess games in a physical artificial environment in order to enrich our dataset substantially. This foundational step furnishes the bedrock components of our data collection pipeline, which is the second part of the project. Once the previous step has been completed we proceed by constructing a preprocessing pipeline tailor-fitted for preparing data for model training, automatizing the meticulous labeling of raw data and curating data consistently. In summary, this thesis is wholly devoted to the betterment and refinement of a machine learning dataset for chess board image recognition, enhancing data collection and automatizing data preprocessing workflows. Insights gleaned from this project bear significance for the continued development of computer vision systems.
