Barcode character defect detection method based on Tesseract-OCR

Zhao, Gang; Lin, Luyu; Chen, Yawen; Liu, Shan; Jie, Chen; Luo, Zhuoran

doi:10.1109/compcomm.2017.8322843

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…This method aims to eliminate products with incorrect spray codes, safeguarding both the reputation and economic interests of the company. The conventional character detection and recognition methods [1] commonly used in industry typically involve the following steps: preprocessing industrial camera images, extracting spray code character regions, character segmentation, extracting character features, using methods such as support vector machines for character classification, and finally comparing with correct text to determine qualification. However, traditional character recognition requires the design of feature extraction templates for different scenes, making it challenging to handle complex task environments with low robustness and inherent limitations.…”

Section: Introductionmentioning

confidence: 99%

A lightweight skew detection algorithm for spray code characters

Fan,

Mo,

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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A lightweight and fast skew detection and recognition method is proposed to address low detection accuracy, slow speed, and the inability to detect skewed spray codes on complex background packaging. Built upon the YOLOv5-obb network, the approach utilizes the Ghost module to lightweight the backbone network, reducing parameters and computations. The introduction of the Slim-neck lightweight feature fusion network structure in the neck further simplifies the model while enhancing detection accuracy. SimAM is added to both the backbone and neck to improve overall detection and recognition rates. In post-processing, a method for merging scene text characters is proposed to address skewed text merging. The final model reduces in size by 39.3%, achieving recognition rates, recall rates, and average precision means of 99.0%, 99.8%, and 99.2% for spray code character detection. The algorithm enables fast and accurate detection of skewed spray codes, providing support for rapid detection in relevant fields.

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Section: Introductionmentioning

confidence: 99%