Attention GAN-Based Method for Designing Intelligent Making System
Lei Shao, Congcong Liang, Ke Wang, Wengang Cao, Wei Zhang, Guan Gui, Hikmet Sari · IEEE Access · 2019
Design of the intelligent making system (iMAS) poses a big technical challenge to recognize both printed and handwriting characters from the scanned images. In this paper, we propose an iMAS method based on the attention generative adversarial network (GAN), which innovatively applies the idea of removing raindrops through attention GAN to remove the printed text first, and then generates the image of handwriting text. The proposed method introduces a classical attention model in the visual field and generates the attention maps which focus only on the important areas through recurrent neural network (RNN), and adopts you only look once at object detection (YOLOv3) method to recognize the characters. Experimental results show that the structural similarity (SSIM) of the image generated by attention GAN is 0.89 and the accuracy of recognition is 91.34%.