## 2 System Description  
As mentioned in the previous section, some specific Mandarin Chinese words in document images can be considered as objects. Consider an input document image \(X=\{\,x(i,j)|1\leq i\leq N_{W},1\leq j\leq N_{H}\}\) of size \(N_{W}\times N_{H}\), where \(x(i,j)\) denotes the pixel value at the coordinate \((i,j)\), \(N_{W}\) and \(N_{H}\) represent the width and height of \(X\), respectively. The goal of the proposed scheme is to detect keywords, \(O^{KW}\), in \(X\). As we know, a keyword (i.e., specific word) is often composed of some key characters (i.e., specific characters) in Mandarin Chinese, i.e., \(O^{KW}=\cup O^{KC}\), where \(O^{KC}\) denotes the key character. For example, the keyword \"\(\cancel{k}\)\" (Name) is composed of two key characters, \"\(\cancel{k}\)\" (Surname) and \"\(\cancel{k}\)\" (First Name), in Mandarin Chinese. This means that a keyword can be found after analyzing these detected key characters. Therefore, it is important to efficiently localize and analyze the key characters from \(X\) for the proposed keyword-detection scheme. As shown in Figure 1, detecting key characters in digitized document images should consider some issues. First, document quality and digitization processing may affect the image properties of digitized document images. As shown in Figure 1a, the visual quality of Figure 1a is poor, and it is tough to analyze degraded document images to detect key characters. This means that the image quality and spatial resolutions of scanned document images are not the same. Second, marginal noise, artificial noise, and random noise may exist in digitized document images. As shown in Figure 1, there is marginal noise around the paper frames, and a seal occurs to be artificial noise on the bottom-right part of Figure 1a. It is no doubt that analyzing the layout of a noisy document image is difficult. Third, these documents may have some popular keywords, but some seldom happen. For example, the keyword \"\(\cancel{k}\)\" (Name) usually exists in document images, but \"\(\cancel{k}\)\" (Applicant) may only happen in some kinds of documents. Fourth, keywords may appear in some possible locations and with different fonts. For instance, the keywords \"\(\cancel{k}\)\" (Name) have different fonts in Figure 1.  
To consider the above issues and the efficiency requirement for keyword detection, the block diagram of the proposed scheme is shown in Figure 2. As shown in Figure 2, the proposed scheme is composed of two parts: key character detection and lexicon analysis. The first part is designed to effectively detect the key characters in a document image. As mentioned in Section 1, CNN-based object-detection methods can be used to localize key characters. These CNN-based object-detection methods can be classified into two classes: one stage and two stages. For two-stage methods, the first step is to find many region proposals, and then these region proposals are analyzed, located, and recognized in the other step. Some frameworks, such as R-CNN, Fast R-CNN, and Faster R-CNN, are two-stage methods [24; 25; 26]. On the contrary, one-stage methods [11; 12; 13; 27], such as SSD, RetinaNet, and YOLO, perform region proposal searching, region proposal localization, and region proposal recognition at the same stage. Though there are more proposals in the one-stage methods compared with the two-stage methods, the one-stage object-detection methods still have a shorter computational time. In addition, key characters with different frequencies of occurrence may occur in a document image, and the number of the key characters may vary in document images. As we can see in Figure 1, some key characters, e.g., \"\(\cancel{k}\)\" (Name), often exist, but some, e.g., \"\(\cancel{k}\)\" (Property), seldom exist in document images. This means that class imbalance may happen so that some key characters with low appearance frequencies may not be detected well.