Deep learning Optical Character Recognition in PCB Dark Silk Recognition

作     者：Bowen Cai Bowen Cai

作者机构：Shanghai Xiashu Intelligent Science Company Co. Ltd. Shanghai China 

出 版 物：《World Journal of Engineering and Technology》 (世界工程和技术（英文）)

年 卷 期：2023年第11卷第1期

页      面：1-9页

学科分类：080903[工学-微电子学与固体电子学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：Deep Learning Dark Silk Computer Vision Pattern Recognition CRAFT Model Printed Circuit Board Electronics Manufacturing Services 

摘      要：For Automatic Optical Inspection (AOI) machines that were introduced to Printed Circuit Board market more than five years ago, illumination technique and light devices are outdated. Images captured by old AOI machines are not easy to be recognized by typical optical character recognition (OCR) algorithms, especially for dark silk. How to effectively increase silk recognition accuracy is indispensable for improving overall production efficiency in SMT plant. This paper uses fine tuned Character Region Awareness for Text Detection (CRAFT) method to build model for dark silk recognition. CRAFT model consists of a structure similar to U-net, followed by VGG based convolutional neural network. Continuous two-dimensional Gaussian distribution was used for the annotation of image segmentation. CRAFT model is good at recognizing different types of printed characters with high accuracy and transferability. Results show that with the help of CRAFT model, accuracy for OK board is 95% (error rate is 5%), and accuracy for NG board is 100% (omission rate is 0%).