Deep learning Optical Character Recognition in PCB Dark Silk Recognition

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were introduced to Printed Circuit Board market more than five years ago, illumination technique and light devices are outdated.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%).