## 5 Results
### Sticker Textual Recognition
For OD, sticker recognition attained a mean Average Precision (mAP) of 0.99 at a 0.5 Intersection Over Union (IoU) and 0.98 for barcodes and QR codes using the same metric. Integrating modern OCR with PP-OCRv3 and modern OD with YOLOv8 within our framework enables the seamless recognition and interpretation of diverse information types on printed stickers, fostering more efficient visual inspection processes in industrial applications. However, our framework exhibits certain limitations. It may encounter difficulties recognizing characters in low-resolution images or when characters are distorted due to suboptimal printing quality. Furthermore, our system may struggle with text featuring unconventional fonts. Certain character sequences also pose challenges, as exemplified in Figure 5. These limitations present opportunities for future research to further refine and enhance the proposed framework. In future work, we aim to extend the capabilities of our framework to handle different types of labels, including those with unconventional fonts or low-quality printing. Additionally, we will explore the integration of additional technologies, such as natural language processing, to improve the extraction and interpretation of information from complex labels. Furthermore, investigating the scalability of our framework to handle large-scale industrial production processes will be a crucial area of future research. In conclusion, our trailblazing framework provides a reliable and efficient solution for automating visual inspection processes in the industrial sector by fusing OCR and OD technologies. We have incorporated the latest and most robust techniques within this framework. By accurately detecting and recognizing both textual and non-textual information on printed stickers, our system significantly streamlines industrial workflows and minimizes manual labor demands. The successful implementation of our framework sets the stage for future progress in automating visual inspection processes and underscores the potential of AI-driven solutions in industrial settings. Conceptualization, G.M., R.G., L.C., G.A. and J.O. ; methodology, G.M., R.G., L.C., R.d.A.F. and G.A. ; software, G.M., R.G., L.C. and G.A. ; validation, G.M., R.G., G.A., L.C. and J.O. ; formal analysis, G.M., R.G., G.A., L.C., R.d.A.F. and J.O. ; investigation, G.M., R.G., G.A. and L.C. ; data curation, G.M., R.G., G.A., L.C., R.d.A.F. and J.O. ; writing--original draft preparation G.M., R.G., G.A., L.C. and R.d.A.F. ; writing--review and editing, G.M., R.G., G.A., L.C., R.d.A.F., H.S. and J.O. ; visualization, G.M., R.G., G.A., L.C., R.d.A.F., H.S. and J.O. ; supervision, I.T., A.P., R.G., C.F. and J.O. ; project administration, I.T., A.P., R.G., C.F. and J.O. ; funding acquisition, I.T. and A.P. All authors have read and agreed to the published version of the manuscript. This paper is a result of the Tesseract Project: \"Computer Vision and Artificial Intelligence-Based Assistant System for Improving the Productive Process of Reading Labels on Modems.\" developed by the Embedded Systems Laboratory at the State University of Amazonas. It is funded by SAGEMCOM BRASIL COMMUNICACOES LTDA under the terms of Brazilian Federal Law n\({}^{\circ}\) 8.387/1991, and its disclosure is in accordance with the provisions of Article 39 of Decree No. 10.521/2020. Not Applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data are available after requisition to the correspondence e-mail. This research and development project was supported by Sagemcom, a French industrial group, world leader in high added value communicating terminals and solutions for the broadband, audio video solution, and energy markets. The authors declare no conflict of interest.