BankNote-Net: Open dataset for assistive universal currency recognition
This work addresses the need for assistive technology for people with low or no vision by providing an open dataset and model for currency recognition, though it is incremental as it builds on existing computer vision methods.
The authors tackled the problem of limited datasets and models for universal currency recognition by collecting 24,826 images across 17 currencies and 112 denominations, and developed a model using supervised contrastive learning that enables few-shot learning and has been deployed in Microsoft's Seeing AI app.
Millions of people around the world have low or no vision. Assistive software applications have been developed for a variety of day-to-day tasks, including optical character recognition, scene identification, person recognition, and currency recognition. This last task, the recognition of banknotes from different denominations, has been addressed by the use of computer vision models for image recognition. However, the datasets and models available for this task are limited, both in terms of dataset size and in variety of currencies covered. In this work, we collect a total of 24,826 images of banknotes in variety of assistive settings, spanning 17 currencies and 112 denominations. Using supervised contrastive learning, we develop a machine learning model for universal currency recognition. This model learns compliant embeddings of banknote images in a variety of contexts, which can be shared publicly (as a compressed vector representation), and can be used to train and test specialized downstream models for any currency, including those not covered by our dataset or for which only a few real images per denomination are available (few-shot learning). We deploy a variation of this model for public use in the last version of the Seeing AI app developed by Microsoft. We share our encoder model and the embeddings as an open dataset in our BankNote-Net repository.