OCR Dataset Curation: Selecting the Ideal Training Corpus OCR Dataset Curation: Selecting the Ideal Training Corpus The accuracy of an Optical Character Recognition (OCR) system largely hinges on the quality and relevance of its training data. While there's an abundance of datasets available for various OCR tasks, curating the perfect corpus for a specific project requires thoughtful deliberation. In this article, we will delve into the art and science of OCR dataset curation, offering insights on how to select the ideal training corpus. Understand Your OCR Goals The starting point for any dataset curation is a clear understanding of the OCR project's goals. Are you building a general-purpose OCR system or one tailored for specific contexts like medical prescriptions, legal documents, or street signs? The context dictates the kind of text variations, fonts, and distortions you'll need in your dataset. Assess Dataset Diversity A well-rounded dataset should encompass: Variet...