Dot Matrix OCR for Bottle Validity Inspection Identifying expiration dates on water bottles for fast industrial processing of a large amount of products mainly affects water bottling factories, food warehouses and supermarkets. The impact of this problem is the distribution of expired bottles of water or their existence on the market. One of the key issues for automatic character readers from bottles is dotted text. Furthermore, the transparent and curved nature of the containing bottle in the presence of water increases the difficulty of the text extraction. An optical character recognition solution using a convolutional neural networks is proposed to solve this issue. The proposed solution segments the input image to extract the bottle, detects the text region of interest, then performs preprocessing operations and finally converts the characters extracted from the region of interest on the bottle in humanreadable characters. The proposed solution has real time performance and it achieves high quality results on the evaluation set.

Vision Inspection of Bottle Caps in Drink Factories Using Convolutional Neural Networks Nowadays, the growth and development of artificial intelligence is more evident than any other time. Consequently, the development of machine vision systems in the industry has been studied extensively. The provision of a highlevel quality control system based on vision inspection in production lines is an important issue. It not only can increase the efficiency, but also derives the necessary tools to gather information about technical errors and faults in the target system. In this paper, we focus on the vision inspection of bottle caps in drink factories using convolutional neural networks. There are different cap encapsulation statuses which are classified as Normal Cap, Unfixed Cap, and No Cap. According to these classes, we can make a decision between either acceptance or rejection of a given bottle. Achieved results show that our optimized finetuning method based on the VGG19 as an EndtoEnd deep learning approach can lead to more than 99% accuracy.