As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Efficient OCR Anomaly Detection and Correction is essential in the legal domain, as it significantly enhances the ability of legal professionals to extract accurate information from documents. This paper presents a novel approach called REMOAC, that improves the performance of a legal text classifier through OCR anomaly detection and correction in legal documents, by actively using state-of-the-art models and explainability techniques. Explainability is a key aspect of our approach, both because we provide transparency and comprehensibility in the OCR anomaly correction process and, more importantly, because we actively use it to improve classification performance.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.