Pixel Labeler: A Pixel-Wise Annotation Tool for Active Learning Music Recognition

Pixel-wise image annotation is a critical but demanding task, particularly in domains where precise labeling is essential for training supervised models. In this work, we present a novel annotation tool designed to assist with Optical Music Recognition (OMR) tasks and to support human-in-the-loop workflows within Active Learning (AL) cycles. The tool focuses on maximizing annotation efficiency and user experience by combining a lightweight interface, usability-driven design, and support for fine-grained, per-pixel semantic labeling. Preliminary results suggest that the proposed interface can reduce the cognitive and operational burden on annotators, offering a viable solution for tasks requiring dense annotations. Feedback from domain users, as well as informal metrics such as annotation time and perceived usability, support the relevance of the design. Overall, this work contributes a flexible and user-centered annotation interface that lays the groundwork for more efficient and effective integration of human input in OMR and AL-based image segmentation workflows.