Document Image Binarization With Stroke Boundary Feature Guided Network

Text is the most crucial element in a document image but is often disconnected in document image binarization. Most of the previous methods based on deep learning do not focus on structure information such as stroke boundary, leading to disconnected strokes when the stroke is ambiguous or weak. In this paper, we propose a multi-task learning with an auxiliary task for learning stroke boundary features in an adversarial manner. The learned boundary features are integrated into the main task for the binarization. Specifically, in the first step, in addition to using shared global location features with the main task, the auxiliary task leverages additional local edges to obtain stroke boundary features. In the second step, we use adversarial loss based on boundary ground truth to supervise the obtained stroke boundary feature in the auxiliary task. The adversarial training is to embed expert knowledge, especially structure information, in the model. In the third step, the learned boundary feature from the auxiliary task supports the main task directly. The fusion module of the main task refines the final binarized image. Experiments show that our method achieves better-preserved stroke and better performance than existing methods on benchmark H-DBCO and DIBCO datasets.