The field of deep learning is rapidly advancing and impacting various industries, including banking. However, there are still challenges when it comes to accurately identifying the denomination of currencies, especially when dealing with issues like variation within the same class of currency and inconsistent lighting conditions. One notable problem is the lack of available data for Kazakhstan's currency. This research paper introduces the Kazakhstan Banknotes Dataset (KZ-BD), which is a unique collection of 4200 carefully annotated images covering 14 different categories. The dataset includes high-resolution images of authentic Kazakhstan Tenge in both coin and paper note forms, ranging from 1 to 20,000 tenge denominations. Each image has undergone strict de-identification and validation procedures, and the dataset is openly accessible to artificial intelligence researchers. This contribution addresses the data gap in deep learning research related to currency identification by offering a comprehensive dataset for Kazakhstan's currency, enabling better evaluation and fine-tuning of machine learning models with real-world data.
Keywords: Banknote recognition; Central Asian currency; Currency detection; Machine learning.
© 2024 The Authors. Published by Elsevier Inc.