TEXT RECOGNITION FOR PCBS_Dataset_Dataset

TEXT RECOGNITION FOR PCBS

Description The document titled "Deep Learning-Based Approaches for Text Recognition in PCB Optical Inspection": A Survey focuses on the challenges and methodologies of automatic text recognition on printed circuit boards (PCBs). It highlights the significance of accurately recognizing component markings and reference designators for effective automatic Bill of Materials extraction and hardware assurance. The survey explores various state-of-the-art end-to-end text recognition approaches, emphasizing the unique challenges posed by PCBs. It also discusses the criticality of data collection with appropriate annotations and presents edge cases with insights for future research. The importance of developing specialized algorithms for PCB marking recognition, integrating self-explainability, and the motivation for further research in this domain are key elements of the study.
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S. Ghosh, M. A. M. Sathiaseelan and N. Asadizanjani, "Deep Learning-Based Approaches for Text Recognition in PCB Optical Inspection: A Survey," 2021 IEEE Physical Assurance and Inspection of Electronics (PAINE), Washington, DC, USA, 2021, pp. 1-8, doi: 10.1109/PAINE54418.2021.9707712
 @INPROCEEDINGS{9707712, author={Ghosh, Shajib and Sathiaseelan, Mukhil Azhagan Mallaiyan and Asadizanjani, Navid}, booktitle={2021 IEEE Physical Assurance and Inspection of Electronics (PAINE)}, title={Deep Learning-Based Approaches for Text Recognition in PCB Optical Inspection: A Survey}, year={2021}, volume={}, number={}, pages={1-8}, doi={10.1109/PAINE54418.2021.9707712}}