Multi-lingual scene text detection and identification is a challenging task in today's world due to the prevalence of many digitized multi-lingual documents, images, and videos. A valuable method for detecting multi-lingual text from natural scene images is proposed which uses the convolutional neural network, namely, You Only Look Once (YOLOv3) as the backbone. The proposed system is more agile than YOLOv3 with the introduction of atrous separable convolution (ASC). The multi-scale prediction in YOLOv3 emphasizes the integration of global features of multi-scale convolutional layers while it overlooks the blend of the multi-scale local region features on the same convolutional layer. To overcome this, ASC is applied to efficiently compute dense local region feature maps, thereby reducing computation complexity substantially. Complete IoU loss, which is an accumulation of overlap area, distance, and aspect ratio, is introduced for enhanced accuracy in bounding box regression, wherein IoU designates the measure of overlap between the predicted and the ground truth bounding boxes. The experimental results show that the proposed system is efficacious in detecting multi-lingual as well as English text from natural scene images.

中文翻译：

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使用敏捷卷积神经网络的多语言文本检测和识别

由于许多数字化的多语言文档、图像和视频的普遍存在，多语言场景文本检测和识别在当今世界是一项具有挑战性的任务。提出了一种从自然场景图像中检测多语言文本的有价值的方法，该方法使用卷积神经网络，即 You Only Look Once (YOLOv3) 作为主干。所提出的系统比 YOLOv3 更灵活，引入了空洞可分离卷积 (ASC)。YOLOv3中的多尺度预测强调多尺度卷积层全局特征的融合，而忽略了同一卷积层上多尺度局部区域特征的融合。为了克服这个问题，应用 ASC 来有效地计算密集的局部区域特征图，从而大大降低了计算复杂度。完全 IoU 损失，它是重叠区域、距离和纵横比的累积，被引入以提高边界框回归的准确性，其中 IoU 指定了预测边界框和真实边界框之间的重叠度量。实验结果表明，所提出的系统在从自然场景图像中检测多语言和英文文本方面是有效的。