FRCA: High-efficiency container number detection and recognition algorithm with enhanced attention

Zhenpeng Wang; Yongli Wang

doi:10.1117/12.2557197

3 January 2020 FRCA: High-efficiency container number detection and recognition algorithm with enhanced attention

Zhenpeng Wang, Yongli Wang

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 1137306 (2020) https://doi.org/10.1117/12.2557197
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

With the rapid development of information technology, text recognition in natural scenes has become a hot topic of current 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 the improved Faster-RCNN network to detect the location of the box number, which increase the attention mechanism in the area generation network (RPN) to speed up the detection speed while ensuring the accuracy. And we use the improved CNN to recognize the box number characters. The experiments on the benchmark dataset and the real dataset prove that compared with the connected region detection method, the Faster-RCNN and VGG-16 combination method, the FasterRCNN and ResNet-101 combined detection method, the accuracy of FRCA model in this paper is better than the former two schemes, and the speed of detection of FRCA network is faster than that of the second and third scheme due to the increase of attention mechanism.

Citation Download Citation

Zhenpeng Wang and Yongli Wang "FRCA: High-efficiency container number detection and recognition algorithm with enhanced attention", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 1137306 (3 January 2020); https://doi.org/10.1117/12.2557197

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