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FRCA: High-efficiency Container Number Detection and Recognition Algorithm with Enhanced Attention

机译：FRCA：高效容器编号检测和识别算法，具有增强的注意力

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摘要

With the rapid development of information technology, text recognition in natural scenes has become a hot topic ofcurrent research. In order to accurately and quickly identify the box number in the container image in the natural scene,this paper proposes a deep learning-based image text recognition model (Faster-RCNN and CNN with Attention(FRCA)), which consists of two stages: box number area detection and box number character recognition. We use theimproved Faster-RCNN network to detect the location of the box number, which increase the attention mechanism in thearea generation network (RPN) to speed up the detection speed while ensuring the accuracy. And we use the improvedCNN to recognize the box number characters. The experiments on the benchmark dataset and the real dataset prove thatcompared with the connected region detection method, the Faster-RCNN and VGG-16 combination method, the Faster-RCNN and ResNet-101 combined detection method, the accuracy of FRCA model in this paper is better than the formertwo schemes, and the speed of detection of FRCA network is faster than that of the second and third scheme due to theincrease of attention mechanism.
机译：随着信息技术的快速发展，自然场景中的文本认可已成为一个热门话题目前的研究。为了准确，快速识别自然场景中的容器图像中的盒子号，本文提出了一种基于深度学习的图像文本识别模型（更快 - RCNN和CNN，注意力（FRCA）），由两个阶段组成：框号区域检测和框号字符识别。我们使用改进了更快的RCNN网络以检测盒号的位置，这增加了引起的注意机制区域生成网络（RPN）加速检测速度，同时确保精度。我们使用改进CNN识别框号字符。基准数据集和实时数据集的实验证明了这一点与连接区域检测方法相比，更快-RCNN和VGG-16组合方法，更快 - RCNN和RESET-101组合检测方法，本文中FRCA模型的准确性优于前者两个方案，并且FRCA网络的检测速度比第二和第三方案的检测速度快于增加注意机制。

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