Another widespread application of computer vision in industry is Optical Character Recognition (OCR) [10]. Contemporary OCR techniques are primarily rooted in deep learning and aim to recognize various characters or numerals in images [10]. These techniques have been increasingly effective, even for complex scripts such as Urdu [11]. OCR technologies can identify characters in different types of documents, such as printed stickers, and can be utilized for Printed Character Recognition (PCR) and Handwritten Character Recognition (HCR) [12]. Recent advancements in HCR have used deep learning for recognizing handwritten words in documents, significantly improving accuracy in this area [13]. PCR is comparatively simpler than HCR due to the limited number of available fonts in PCR as opposed to the diverse handwriting styles found in HCR [14]. However, it is often necessary to identify documents or printed stickers containing arbitrary characters, such as hexadecimal identifiers or passwords with special characters [14].  
Paddle OCR (PP-OCR) is a versatile and powerful solution for PCR and HCR, effectively addressing these challenges [15]. This tool offers a comprehensive OCR system comprising three components: text detection, detection boxes, and text recognition [15]. Text detection identifies text segments, detection boxes correct text orientation, and text recognition classifies each character present in a text segment [15; 16].  
Industrial applications frequently face challenges related to non-textual and textual information recognition. These challenges can encompass variations in lighting conditions, low-resolution images, and complex image patterns, all of which can negatively impact the performance of OD and OCR systems. To tackle this issue, many industries rely on human visual inspection (VI) for information recognition. However, VI comes with its own set of problems, such as visual fatigue and inconsistent results.  
Despite the advancements in sticker textual and non-textual information detection, a well-established framework is yet to be developed. OD integrated with OCR technologies can be harnessed to create a robust industrial application in this area. In this paper, we propose an innovative framework that combines OCR and OD to automate visual inspection processes in an industrial context. Our primary contribution is a comprehensive framework that detects and recognizes both textual and non-textual information on printed stickers within a company. Our system employs the latest AI tools and technologies for OCR, such as YOLOv8 and PaddleOCRv3, and it is capable of processing printed stickers beyond traditional PCR, extracting additional information such as barcodes and QR codes present in the image. We emphasize that the proposed framework was designed to operate without human intervention. The motivation behind this study was to automate a process currently performed manually, with visual inspection, by operators in the industry. Through introducing automation to the visual inspection of modern stickers, our aim is to enhance both the speed and accuracy of this procedure.  
The remainder of this paper is structured as follows: Section 2 introduces the fundamental concepts of OD and OCR, with a particular emphasis on YOLO and PP-OCR. Section 3 outlines the relevant literature. Section 4 describes the databases, the OD approach, and the proposed framework. Section 5 delves into the training outcomes and performance evaluation. Lastly, Section 6 provides concluding remarks and highlights possible avenues for future research.