论文标题
OCR对以自我为中心数据的评估
An Evaluation of OCR on Egocentric Data
论文作者
论文摘要
在本文中，我们评估了以自我为中心数据的最新OCR方法。我们在Epic-Kitchens图像中注释文本，并证明现有的OCR方法与旋转的文本难度，这在处理的对象上经常观察到。我们引入了一个简单的旋转和合并过程，可以将其应用于预先训练的OCR模型，该模型将标准化的编辑距离误差减半。这表明未来的OCR尝试应将旋转纳入模型设计和培训程序中。
In this paper, we evaluate state-of-the-art OCR methods on Egocentric data. We annotate text in EPIC-KITCHENS images, and demonstrate that existing OCR methods struggle with rotated text, which is frequently observed on objects being handled. We introduce a simple rotate-and-merge procedure which can be applied to pre-trained OCR models that halves the normalized edit distance error. This suggests that future OCR attempts should incorporate rotation into model design and training procedures.
