At present, text orientation is not diverse enough in the existing scene text datasets. Specifically, curve-orientated text is largely out-numbered by horizontal and multi-oriented text, hence, it has received minimal attention from the community so far. Motivated by this phenomenon, we collected a new scene text dataset, Total-Text, which emphasized on text orientations diversity. It is the first relatively large scale scene text dataset that features three different text orientations: horizontal, multi-oriented, and curve-oriented. In addition, we also study several other important elements such as the practicality and quality of ground truth, evaluation protocol, and the annotation process. We believe that these elements are as important as the images and ground truth to facilitate a new research direction. Secondly, we propose a new scene text detection model as the baseline for Total-Text, namely Polygon-Faster-RCNN, and demonstrated its ability to detect text of all orientations. Images of Total-Text and its annotation are available at https://github.com/cs-chan/Total-Text-Dataset.

中文翻译：

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Total-Text：场景文本检测中的方向稳健性

目前，现有场景文本数据集中的文本方向不够多样化。具体来说，面向曲线的文本在很大程度上不如横向和多向文本，因此，到目前为止，它很少受到社区的关注。受此现象的影响，我们收集了一个新的场景文本数据集Total-Text，强调文字方向的多样性。它是第一个相对大规模的场景文本数据集，具有三个不同的文本方向：水平，多方向和曲线方向。此外，我们还研究了其他几个重要元素，例如地面实况的实用性和质量，评估协议以及注释过程。我们认为，这些元素与图像和地面真实情况一样重要，以促进新的研究方向。其次，我们提出了一个新的场景文本检测模型，作为Total-Text的基线，即Polygon-Faster-RCNN，并展示了其检测所有方向的文本的能力。可在https://github.com/cs-chan/Total-Text-Dataset获得Total-Text的图像及其注释。