Secondary Screening Detection optimization Method for Electronic Components Based on Artificial Intelligence

In view of the problem about low components code recognition accuracy, low detection efficiency, long detection time and high detection cost in electronic components secondary screening detection, this paper proposes an optimization method of electronic components secondary screening based on artificial intelligence model. Firstly, use the gradient-based decision tree model to calculate the relationship between the detection items, find the optimal secondary screening combination scheme for electronic components, then complete electronic component code recognizing based on CTPN+Tesseract-OCR deep learning model, improve the accuracy of electronic component code recognition. The cases analysis shows that the proposed method in this paper has a higher digital recognition rate, fewer detection times in the same batch of products, indicating the effectiveness and applicability of this method.(Abstract)