# A Study on DNN-Based Practical Model for Predicting Spot Color
## 1 Introduction  
### Backgrounds  
Product packaging does more than just attract the consumer's attention. It provides essential information about the product, allowing consumers to make quick and informed decisions. But a subtle but critical aspect of packaging is its color. Think of Coca-Cola's bold red or Starbucks' signature green, and it becomes clear that packaging color alone can evoke a specific product or brand [1]. As a result, many brands are recognizing color not just as an aesthetic choice, but as a powerful communication tool that is integral to their brand identity. Consistent color presentation is a priority for them, regardless of their global location.  
Given the importance of color, the use of spot colors becomes essential. Spot colors refer to predefined, specific colors. A leading example of standardization is the Pantone Matching System (PMS) [2]. Developed by Pantone, it is used worldwide for color communication. Within the system, each spot color is identified by its unique number and name, allowing for consistent reference and reproduction around the world. This allows brands to ensure that their distinctive colors are expressed consistently.  
But the reality in the packaging printing industry, where mass production is essential, is relatively complex. The dominant method in this field is the CMYK model, which is based on the use of four primary colors: Cyan (C), Magenta (M), Yellow (\(\gamma\)), and Key (Black) (K). These inks are used in varying proportions to produce a wide range of colors. The CMYK model works on the principle of subtractive color mixing, where colors are created by subtracting different amounts of light. Unlike additive color models, which work by adding light (like screens), subtractive color models work by absorbingsome wavelengths of light and reflecting others. By combining these four inks in varying proportions, a wide range of colors can be reproduced on the printed material. This method is particularly economical for high-volume printing, ensuring consistent quality across large batches of printed materials [3]. However, complexity is introduced by the use of spot colors, which are specific pre-mixed inks used to achieve precise shades that are not easily achieved with CMYK mixing. These standard spot colors, such as those from the Pantone Matching System (PMS), require additional processes to accurately reproduce. In particular, the manual mixing and testing of these spot colors by colorists can be error-prone, resulting in discrepancies between intended and final print colors. This issue is particularly challenging for smaller printers, where operator expertise and subjective judgment play an important role. Factors such as lighting conditions can also affect their perception, leading to inconsistencies and inefficiencies in the mixing process. These inconsistencies result in wasted resources and increased costs. From the customer's perspective, the use of spot colors is to achieve consistent coloration, so the inconsistent print results are undesirable from a quality control standpoint.  
In response to these challenges, this study presents a deep learning-based model that predicts ink mixing results without actual ink mixing in packaging printing. The use of these techniques has become more prevalent in the manufacturing domain, increasing productivity in recent years [4]. Based on actual process data, the model is designed to reduce the human color perception bias.