We present a novel single-shot text detector that directly outputs word-level
bounding boxes in a natural image. We propose an attention mechanism which
roughly identifies text regions via an automatically learned attentional map.
This substantially suppresses background interference in the convolutional
features, which is the key to producing accurate inference of words,
particularly at extremely small sizes. This results in a single model that
essentially works in a coarse-to-fine manner. It departs from recent FCN- based
text detectors which cascade multiple FCN models to achieve an accurate
prediction. Furthermore, we develop a hierarchical inception module which
efficiently aggregates multi-scale inception features. This enhances local
details, and also encodes strong context information, allow- ing the detector
to work reliably on multi-scale and multi- orientation text with single-scale
images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 bench-
mark, advancing the state-of-the-art results in [18, 28]. Demo is available at:
this http URL

本研究提出了一种新颖的单步文字检测器，该检测器直接在自然图像中输出单词级别的边界框。通过自动学习的注意力映射，我们提出了一种注意机制，粗略地识别文本区域。此外，我们还开发了分层 Inception 模块，可以有效地聚合多尺度 Inception 特征，从而使该检测器能够可靠地在单尺度图像上处理多尺度和多方向文本。我们的文本检测器在 ICDAR 2015 基准测试中取得了 77％的 F-measure，超越了先前研究的最新成果。