# A Study on DNN-Based Practical Model for Predicting Spot Color
## 1 Introduction
### Related Works
Meanwhile, as academic interest in machine learning (especially neural networks) has grown over the past few decades, studies have been conducted in the printing industry to apply these techniques. In a cross-disciplinary effort within the industry, [PERSON] and [PERSON] (2019) proposed a neural network-based image pre-processing method to cope with the quality degradation caused by thicker printing plates, and optimized the parameters of the neural network using evolutionary algorithms. [PERSON] et al. (2018) used an ANN model to predict the percentage frequency of common spoilage problems in offset printing based on information about the ink used in the print, the machine, the paper, and the design, while [PERSON] et al. (2020) used machine learning techniques to analyze the relationship between ink thickness and other process variables to predict ink thickness as a function of process variables in an offset press sensitive to environmental conditions. [PERSON] et al. (2021) used DNN to improve traditional visual inspection methods in the production of gravure cylinders used in printing. It achieved 98.4% classification accuracy on files used to engrave cylinders and on surface scans of produced cylinders. [PERSON] et al. (2020) used DCNN to extract features from print samples with an unbalanced number of samples according to defect types, and then classified the defect types into four categories. Based on data collected from actual printed products at the factory, a classification accuracy of 96.86% was achieved. [PERSON] et al. (2021) evaluated CNN-based models to automate traditional manual labor in gravure pattern classification and found a model with a classification accuracy of 95.2%. [PERSON] et al. (2020) presented a real-time deep learning approach for detecting differences between reference and printed images in variable data printing. [PERSON] et al. (2020) used ResNet-50 to classify the conditions under which an image was printed based on the moire pattern present in the print. The results suggest that deep learning technology can be applied to printing to prevent document forgery. More recently, with the advent of printed electronics technology, which uses functional inks to produce electronic circuits, [PERSON] et al. (2020) used machine learning models to analyze the movement of ink droplets and predict the quality of jetting in inkjet printing methods. [PERSON] et al. (2020) proposed a print quality assessment system for printed electronics using gravure offset printing. The system uses a DCNN with skip connections to classify print quality and a pre-trained object detection model to detect defects in the print. Regarding color, [PERSON] and [PERSON] (2020) investigated a neural network approach to learning the relationship between colorant and color as an alternative to the K-M model for the recipe prediction problem. [PERSON] (2018) proposed a color reproduction method for dye sublimation printers using a neural network that learns the mapping from the RGB coordinates of the printed color to the CMY value, the primary color of the print, using a back-propagation technique. [PERSON] et al. (2020) presented an ANN model as a function for converting device-independent CIELAB values from digital images to CMYK spaces for printing. [PERSON] and [PERSON] (2020) improved the accuracy of predicting CMYK values for reproducing printed sample colors by introducing a cascade-forward neural network on training samples divided into multiple subgroups by a competitive neural network. Due to the non-uniformity of the LAB color space, [PERSON] and [PERSON] (2020) divided the printed color sample into 10 parts based on hue angle and developed a model that uses a separate neural network for each part to learn the complex relationship between printed colors and digital pixel values (RGB). However, these previous studies are mainly limited to data printed with predetermined CMYK inks, which is not directly applicable to the printing environment of an integrated packaging printer.