傳統大型自動化生產設備由海外原廠進口機具，透過操作面板進行參數調整，
進行自動化生產製造，而大型面板參數與台灣原物料環境與商品容錯性差異有些許
差異，往往需由人工紀錄操作，建立警示參數標準。
目前市場中唯一可以進行傳統機台操作紀錄擷取方式，為透過人力進行系統面
板資訊紀錄，往往費時費力，並且人力成本過高，對產線人員來說工作負擔過於龐
大。希望透過影像辨識，由外部不破壞機台狀態下，進行相關數據擷取分析改善傳
統機台無法數據雲端化的技術瓶頸與障礙。
本論文透過改良ESRGAN網路對大型機台面板進行影像細節紋理還原，進行相
關大型機台面板數據擷取分析並結合google OCR 將面板影像轉為參數數值，將擷
取結果結合數據分析，提供更精準大型機台面板參數標準資訊。同時在不同干擾源
狀態下，仍能進行數據萃取分析並完成自動化輸入圖片輸出偵測的txt文件，有效協
助產線人員記錄機台面板參數，降低產線人員的工作負擔。

Traditional mainframe panel automatic production equipment is imported from overseas original factories, and the parameters are adjusted through the operation panel for automatic production and manufacturing. However, the parameters of mainframe panels are slightly different from those of Taiwan’s raw material environment and product fault tolerance, and often need to be manually recorded. Warning parameter standard.
At present, the only method in the market that can capture the operation records of traditional machines is to manually record the system panel information, which is often time-consuming and laborious, and the labor cost is too high, which is too heavy for the production line personnel. It is hoped that through image recognition, relevant data acquisition and analysis can be performed from the outside without destroying the machine, to improve the technical bottlenecks and obstacles that cannot be cloud-based for traditional machines.
This research improves the ESRGAN network to restore the image details and textures of mainframe panels, conducts relevant mainframe panel data acquisition and analysis, combines google OCR to convert panel images into parameter values, and combines the captured results with data analysis to provide more accurate mainframe panel parameter standard information. At the same time, under different interference source conditions, data extraction and analysis can still be carried out and txt files for automatic input image output detection can be completed, which effectively assists production line personnel to record machine panel parameters and reduces the workload of production line personnel.

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