Disentangled Contour Learning for Quadrilateral Text Detection

Precise detection of quadrilateral text is of great significance for subsequent recognition, where the main challenge comes from four distorted sides. Existing methods concentrate on learning four vertices to construct the contour. However, vertices are dummy intersections entangled by their neighbor sides. The regression of each vertex would simultaneously affect its two neighbor sides. As a result, the originally independent side would be influenced by two different vertices which further inevitably disturb other sides. The above entangled vertices learning suppresses the learning efficiency and detection performance. In this paper, we proposed disentangled contour learning network (DCLNet) to focus on clear regression of each individual side disentangled from the whole quadrilateral contour. The side is parameterized by a linear equation that disentangled in the polar coordinates for easier learning. With tailored Ray-IoU loss and sine angle loss, DCLNet could better learn the representation of each disentangled side without being disturbed by others. The final quadrilateral text contour is easily constructed by intersecting the predicted linear equations of sides. Empirically, the proposed DCLNet achieves state-of-the-art detection performances on three scene text benchmarks. Ablation study is also presented to demonstrate the effectiveness of proposed disentangled contour learning framework.