Abstract
Pursuing efficient text shape representations helps scene text detection models focus on compact foreground regions and optimize the contour reconstruction steps to simplify the whole detection pipeline. Current approaches either represent irregular shapes via box-to-polygon strategy or decomposing a contour into pieces for fitting gradually, the deficiency of coarse contours or complex pipelines always exists in these models. Considering the above issues, we introduce EdgeText to fit text contours compactly while alleviating excessive contour rebuilding processes. Concretely, it is observed that the two long edges of texts can be regarded as smooth curves. It allows us to build contours via continuous and smooth edges that cover text regions tightly instead of fitting piecewise, which helps avoid the two limitations in current models. Inspired by this observation, EdgeText formulates the text representation as the edge approximation problem via parameterized curve fitting functions. In the inference stage, our model starts with locating text centers, and then creating curve functions for approximating text edges relying on the points. Meanwhile, truncation points are determined based on the location features. In the end, extracting curve segments from curve functions by using the pixel coordinate information brought by truncation points to reconstruct text contours. Furthermore, considering the deep dependency of EdgeText on text edges, a bilateral enhanced perception (BEP) module is designed. It encourages our model to pay attention to the recognition of edge features. Additionally, to accelerate the learning of the curve function parameters, we introduce a proportional integral loss (PI-loss) to force the proposed model to focus on the curve distribution and avoid being disturbed by text scales.
Abstract (translated)
追求高效的文本形状表示有助于场景文本检测模型聚焦于紧致的前景区域，并优化轮廓重建步骤以简化整个检测流程。当前的方法要么通过盒到多边形策略来表示不规则形状，要么将一个轮廓分解成多个片段逐步拟合。这些方法中往往存在粗略轮廓或复杂流程的问题。 为了解决上述问题，我们提出了EdgeText模型，旨在紧凑地匹配文本轮廓同时缓解过度的轮廓重建过程。具体而言，观察到文本的两条长边可以视为平滑曲线，这使得我们可以构建连续且平滑覆盖文本区域的边缘以代替分段拟合的方式，从而避免现有模型中的两个局限性。受到这一发现启发，EdgeText将文本表示形式化为参数化曲线拟合函数的问题。 在推理阶段，我们的模型首先定位文字中心，然后基于这些点创建用于近似文字边界的曲线函数。同时，根据位置特征确定截断点。最后，利用由截断点带来的像素坐标信息从曲线函数中提取曲线段来重建文本轮廓。 此外，考虑到EdgeText对文本边缘的深度依赖性，我们设计了一种双边增强感知（BEP）模块，以鼓励模型注意边缘特性的识别。为了加速学习曲线函数参数的速度，我们引入了比例积分损失（PI-loss），使提出的模型专注于曲线分布并避免受到文字尺度的影响。
URL
https://arxiv.org/abs/2504.04001
