Real-Time Automated Detection of Errors in the Product Labels Using Image Processing and OCR

In the realm of product manufacturing and distribution, ensuring accurate and compliant product labeling is critical for both regulatory compliance and consumer safety. This research introduces a novel methodology for the automated identification of inaccuracies in product labels through the integration of image processing and Optical Character Recognition (OCR). Image processing techniques are used to recognize and analyze visual components including barcodes, logos, and product images, while OCR accurately transcribes text from the label images. The system uses a rule-based framework to compare the retrieved data to reference template in order to improve error detection. Erroneous or missing information, irregular formatting, or disparities in the visual representation of the label all constitute deviations from the intended content and should set up alarms for further investigation. The methodology has been tested and validated on a custom dataset of product labels from a variety of sectors. The proposed model is implemented in real-time, and its recognition accuracy is found to be 78%. The system's error-detection performance is impressive, making it a viable option for producers and shippers looking to standardize their quality assurance procedures and reduce their exposure to legal liability. The proposed approach not only improves accuracy but also greatly decreases the manual work typically involved with label verification, hence boosting overall operational efficiency in consumer goods production and delivery.