Intelligent Micron Optical Character Recognition of DFB Chip Using Deep Convolutional Neural Network

Wang, Xudong; Li, Yebin; Liu, Juanxiu; Zhang, Jing; Du, Xiaohui; Liu, Lin; Liu, Yong

doi:10.1109/tim.2022.3154831

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…However, in real-world applications, only using one network cannot cover the industrial application. The process of industrial OCR is composited by at least four steps: image acquisition, character segmentation, character recognition, and post-processing [11][12][13][14][15][16][17]19]. At the mean, the performance of these steps is inseparable because the output of one step is the input of the next step.…”

Section: Industrial Ocrmentioning

confidence: 99%

“…The OCR systems are adept at extracting textual data from images, documents, or physical objects and transcribing optical text into digital formats, thus playing a pivotal role in the modernization of manufacturing processes [11][12][13][14][15][16]. The implementation of OCR technology spans diverse industrial sectors, including automotive [16], iron [12,15], printed circuit board (PCB) [11,14], and pharmaceuticals [17]. The efficacy of OCR in these sectors can be attributed to three primary reasons:…”

Section: Introductionmentioning

confidence: 99%

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The Industrial Application of Artificial Intelligence-Based Optical Character Recognition in Modern Manufacturing Innovations

Tang,

Lee,

Jung

2024

Sustainability

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This paper presents the development of a comprehensive, on-site industrial Optical Character Recognition (OCR) system tailored for reading text on iron plates. Initially, the system utilizes a text region detection network to identify the text area, enabling camera adjustments along the x and y axes and zoom enhancements for clearer text imagery. Subsequently, the detected text region undergoes line-by-line division through a text segmentation network. Each line is then transformed into rectangular patches for character recognition by the text recognition network, comprising a vision-based text recognition model and a language network. The vision network performs preliminary recognition, followed by refinement through the language model. The OCR results are then converted into digital characters and recorded in the iron plate registration system. This paper’s contributions are threefold: (1) the design of a comprehensive, on-site industrial OCR system for autonomous registration of iron plates; (2) the development of a realistic synthetic image generation strategy and a robust data augmentation strategy to address data scarcity; and (3) demonstrated impressive experimental results, indicating potential for on-site industrial applications. The designed autonomous system enhances iron plate registration efficiency and significantly reduces factory time and labor costs.

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