An Efficient Industrial Product Serial Number Recognition Framework

Abstract

The serial number is essential for product tracking in industries. Without serial numbers, the control of production is lost. Optical character recognition (OCR)—a technique for extracting characters' features and utilizing these features to recognize them in digital images—is widely used to identify serial numbers and improve product tracking efficiency. However, it is relatively challenging for OCR to adapt to digital images containing unclean environments in industries. Images taken in such environments can include various objects, making it hard for OCR models to focus on serial numbers. In this paper, we propose an industrial product serial number recognition framework, which can efficiently adapt complex structures of the background environment and recognize serial numbers in industries. The framework includes the data preprocessing stage, detection stage, and recognition stage. Eventually, our framework reaches nearly four times better on the testing dataset than the baseline score and increases the efficiency of product tracing in industries.