Research and Application of Image Enhancement and Correction Algorithm of Handwritten Steel Plate Numbering Based on Machine Vision

doi:10.19678/j.issn.1000-3428.0068471

Abstract

Abstract:

Correct identification and inspection of steel plate numbers are major conditions for achieving automated production in production lines. In recent years, many production lines have been equipped with inkjet printers at the material preparation positions for automatically marking material numbers. Spray-printed handwriting is clear and heat-resistant, and the use of steel plate number recognition equipment can achieve a recognition rate of nearly 100% without application. However, due to equipment failures or limited funding and space, installing printing equipment and relying only on manual handwriting to mark numbers on the surfaces of steel plates are sometimes impossible. Compared with spray-printed numbers, handwritten numbers involve complex features, such as arbitrary writing, continuous strokes, and distorted handwriting, which limit the accuracy of the recognition system. Due to poor recognition performance, relying on manual visual inspection to assist in recognition is often necessary, which affects the implementation of material-tracking automation. To improve the recognition of handwritten steel plate numbers, this study introduces improvements to the traditional machine learning Optical Character Recognition (OCR) text-region detection algorithm. An algorithm for image enhancement and distortion correction is also proposed based on the characteristics of handwritten steel plate numbers. These algorithms are designed to improve the image quality and shapes of handwritten steel plate numbers, thereby increasing recognition accuracy. Overall, the study aims to improve the recognition of handwritten steel plate numbers to solve the difficulties associated with automated production. Through image enhancement and correction, the recognition system can process handwritten steel plate numbers more effectively, further promoting the automated implementation of material tracking.

Key words: Optical Character Recognition(OCR), steel plate numbering identification, handwriting OCR area correction, OCR image preprocessing, automatic recognition

摘要：

钢板号的正确识别检查是实现生产线自动化生产的重要基础条件之一。近年来, 许多生产线在备料位置配备了喷印机用于自动标记物料编号。喷印的字迹清晰且耐高温, 在没有涂抹的情况下使用钢板号识别设备可以实现接近100%的识别率。然而, 由于喷印设备故障或受限于资金和空间等原因, 有时无法安装喷印设备, 只能依赖人工手写的方式在钢板表面标记编号。与喷印编号相比, 手写编号存在书写随意、连笔、字迹歪斜扭曲等复杂情况, 这些因素限制了识别系统的准确性。鉴于识别效果较差, 通常需要依赖人工目测来辅助识别, 从而影响了物料跟踪自动化的实施效果。为了提升手写钢板号的识别效果, 对传统机器学习光学字符识别(OCR)文本区域检测算法进行改进研究, 并针对手写钢板号的特征, 提出一种图像增强和扭曲矫正处理的算法。应用结果表明, 该算法可以改善手写钢板号的图像质量和形状, 提高识别的准确性。该研究旨在提升手写钢板号识别效果, 以解决自动化生产中的难题。通过图像增强和矫正处理, 使识别系统更好地处理手写钢板号, 推动物料跟踪的自动化实施。

关键词: 光学字符识别, 钢板号识别, 手写OCR区域校正, OCR图像预处理, 自动识别

Kuanguang XU, Dongyu HE, Bing HAN, Yujia LIU, Jiadong LI. Research and Application of Image Enhancement and Correction Algorithm of Handwritten Steel Plate Numbering Based on Machine Vision[J]. Computer Engineering, 2024, 50(4): 350-356.

徐宽广, 何东隅, 韩冰, 刘宇佳, 李家栋. 基于机器视觉的手写钢板号图像增强及矫正算法研究与应用[J]. 计算机工程, 2024, 50(4): 350-356.

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Figures/Tables 15

Fig.1 Schematic diagram of detection area

Fig.2 Steel plate numbering identification detection procedure

Fig.3 Original image

Fig.4 Corrected image after perspective transformation

Fig.5 Color scale enhancement process

Fig.6 Character region detection effect

Fig.7 Outline character line extraction procedure

Fig.8 Character region perspective transformation

Fig.9 Centerline extraction

Fig.10 Fitting character line skeleton curve

Fig.11 Schematic diagram of the center horizontal line position

Fig.12 Final correction result

Fig.13 Material calibration verification effect

Fig.14 Recognition model training process

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