TextDCTv2: Scene Text Detection With Patch-Based Discrete Cosine Transform Representation

Regression-based scene text detection methods, which mainly locate text by regressing parameterized curves, have become popular recently. However, parameterized curves typically require a pre-defined starting coordinate and direction for the contour curves to ensure consistent learning, which limits their flexibility in representing arbitrary-shaped text. Methods based on the parameterized mask would be a promising alternative. Text instance mask naturally has consistency and is less noisy than contour vertices representing the parameterized curves. However, this branch of methods still remains less studied. In this work, we propose a novel scene text detection framework, named TextDCTv2, which adopts the discrete cosine transform (DCT) to represent each text instance mask as a compact vector. Instead of simply applying DCT to the entire text instance mask, we split the mask into multiple patches and perform DCT on each patch individually. This allows us to focus on regions that are difficult to detect and then refine them without affecting other patches. Specifically, each patch is first transformed into a frequency domain matrix using DCT. Then, we introduce a direct current identifier to detect foreground, background and challenging patches based on direct current coefficients, i.e., the zero-frequency components of the DCT matrix that reflect the overall brightness of a patch. Next, for challenging patches, we employ a lightweight refinement regressor to further correct their DCT vectors and generate precise text boundaries. Through these designs, we achieve an efficient and precise text mask representation. Extensive experiments prove that our method is superior to most of existing methods on various datasets. Specifically, TextDCTv2 achieves an F-measure of 88.7 at 20.3 frames per second (FPS) on the Total-Text dataset, and an Fmeasure of 87.8 at 30.3 FPS on the CTW1500 dataset.