论文标题
ABCNET：实时场景文本与自适应bezier-curve网络斑点
ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network
论文作者
论文摘要
场景文本检测和识别已受到越来越多的研究关注。现有方法可以大致分为两组：基于字符和基于细分的基于分割。这些方法对于字符注释而言是昂贵的，或者需要维护复杂的管道，这通常不适合实时应用。在这里，我们通过提出自适应Bezier-Curve网络（ABCNET）来解决该问题。我们的贡献是三个方面：1）首次通过参数化的bezier曲线适应任意形状的文本。 2）我们设计了一个新型的bezierAlign层，用于提取具有任意形状的文本实例的准确卷积特征，与以前的方法相比，精度显着提高了精度。 3）与标准边界框检测相比，我们的Bezzier曲线检测引入了可忽略的计算开销，从而在效率和准确性方面具有优势。在任意形状的基准数据集（即总文本和CTW1500）上进行的实验表明，ABCNET达到了最新的准确性，同时显着提高了速度。特别是，在总文本上，我们的实时版本的速度比最近的最新方法快10倍，具有竞争性的识别精度。代码可从https://tinyurl.com/adelaidet获得
Scene text detection and recognition has received increasing research attention. Existing methods can be roughly categorized into two groups: character-based and segmentation-based. These methods either are costly for character annotation or need to maintain a complex pipeline, which is often not suitable for real-time applications. Here we address the problem by proposing the Adaptive Bezier-Curve Network (ABCNet). Our contributions are three-fold: 1) For the first time, we adaptively fit arbitrarily-shaped text by a parameterized Bezier curve. 2) We design a novel BezierAlign layer for extracting accurate convolution features of a text instance with arbitrary shapes, significantly improving the precision compared with previous methods. 3) Compared with standard bounding box detection, our Bezier curve detection introduces negligible computation overhead, resulting in superiority of our method in both efficiency and accuracy. Experiments on arbitrarily-shaped benchmark datasets, namely Total-Text and CTW1500, demonstrate that ABCNet achieves state-of-the-art accuracy, meanwhile significantly improving the speed. In particular, on Total-Text, our realtime version is over 10 times faster than recent state-of-the-art methods with a competitive recognition accuracy. Code is available at https://tinyurl.com/AdelaiDet
