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Text Detection in Natural Scene Images with Text Line Construction

机译：具有文本行构造的自然场景图像中的文本检测

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This paper implements a new text region recognition algorithm that can accurately localize image text regions in natural image with complex background. The method is mainly based on the anchor mechanism of the faster R-CNN, taking into account the special features of the text area relative to other object detect tasks, so as to convert the text area detection task in the image into a general object detection task for the small area text. In this way, we can detect the text proposal directly in the convolutional feature map of the neural network, and it can simultaneously predict the texton- text score of the proposal and the coordinates of each proposal in the image. Then we propose a text line construction algorithm that can combine the text regions into complete text line blocks, thus greatly improving the accuracy and reliability of our text detection model. Our text detector also works accurately in multi-scale and multi-lingual text detection tasks. It achieves 0.86 F-measure and 0.78 F-measure on the ICDAR 2011 and ICDAR 2013 benchmarks, which also confirms the accuracy of our model.
机译：本文实现了一种新的文本区域识别算法，该算法可以在具有复杂背景的自然图像中准确定位图像文本区域。该方法主要基于更快的R-CNN的锚定机制，并考虑到文本区域相对于其他对象检测任务的特殊特征，从而将图像中的文本区域检测任务转换为一般的对象检测小区域文字的任务。这样，我们可以直接在神经网络的卷积特征图中检测文本建议，并且可以同时预测建议的文本/非文本分数以及图像中每个建议的坐标。然后，我们提出了一种文本行构造算法，该算法可以将文本区域组合成完整的文本行块，从而大大提高了文本检测模型的准确性和可靠性。我们的文本检测器还可以在多种规模和多种语言的文本检测任务中准确运行。它在ICDAR 2011和ICDAR 2013基准上达到0.86 F-measure和0.78 F-measure，这也证实了我们模型的准确性。

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