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Open Access

ARTICLE

Detection and Recognition of Spray Code Numbers on Can Surfaces Based on OCR

Hailong Wang*, Junchao Shi
School of Computer Science, Zhongyuan University of Technology, Zhengzhou, 450007, China
* Corresponding Author: Hailong Wang. Email: email
(This article belongs to the Special Issue: Deep Learning and Computer Vision for Industry 4.0 and Emerging Technologies)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.057706

Received 26 August 2024; Accepted 24 October 2024; Published online 19 November 2024

Abstract

A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition. In the coding number detection stage, Differentiable Binarization Network is used as the backbone network, combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect. In terms of text recognition, using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise. In addition, model pruning and quantization are used to reduce the number of model parameters to meet deployment requirements in resource-constrained environments. A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site, and a transfer experiment was conducted using the dataset of packaging box production date. The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor, and can accurately identify the coding numbers at high production line speeds. The Hmean value of the coding number detection is 97.32%, and the accuracy of the coding number recognition is 98.21%. This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.

Keywords

Can coding recognition; differentiable binarization network; scene visual text recognition; model pruning and quantification; transport model

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