## 1 Introduction
In the proposed method [5], the color information is used to identify camera-captured screen images, a bilateral filter is used to reduce the effect of noise in captured screen images, and then the color and spatial information are used to initially and roughly locate the candidate region. After skew correction, a template-matching algorithm is used to find special symbols for locating the ROI. Then, the patient identification information can be  
Figure 1: Three examples of digitized document images in Taiwan: (**a**) case 1, (**b**) case 2, and (**c**) case 3.  
recognized in the ROI. Unfortunately, traditional image layout analysis methods may not deal with noisy document images well. It is expected that poor image layout analysis or document segmentation may affect the following recognition process [6]. Compared with traditional machine-learning methods developed based on hand-crafted features, Deep Neural Networks (DNN) [7; 8] have received more and more attention due to their excellent performance in image classification, speech recognition, fraud detection, and so on. Many CNNs, such as AlexNet, VGG, GoogleNet, and ResNet, demonstrate that they have better capabilities to extract multi-level visual features for improving the accuracy of classification models [7; 8; 9; 10; 11; 12; 13]. As for document image analysis and scene image analysis, there are some deep-learning-based methods [6; 14; 15; 16; 17; 18]. For example, a deep-learning approach is proposed based on a fully convolutional network (FCN) [9] for document segmentation [15]. In ref. [16], a fast CNN-based method is proposed to automatically perform layout analysis for document images. In the existing method [16], a document image is segmented into some blocks, and these blocks are classified into three categories, i.e., text, table, and image, based on a CNN. In [6], some deep CNNs such AlexNet, VGG-16, GoogleNet, and ResNet-50 are used to distinguish document images into some categories such as emails, news articles, and invoices for document image classification. In ref. [17], an existing method, EAST, similar to FCN [9], is exploited to detect text regions in scene images. In EAST, multi-level feature maps are extracted, merged, and fed into the output layer for pixel-level predictions of text regions. Unfortunately, the text detection method [17] may not be suitable to analyze these document images, which usually have many Mandarin Chinese words in Figure 1.  
As far as we are concerned, object detection plays an important role in vision-based applications such as face detection, traffic sign detection, character/digit detection, person re-identification, animal detection, and meter reading [5; 19; 20; 21; 22]. So far, in addition to traditional machine-learning-based methods, there are some CNN-based object-detection algorithms, including R-CNN (region-based convolutional neural network), Faster R-CNN, you only look once (YOLO), and a single-shot multiBox detector (SSD), RetinaNet et al. [23]. For example, an automatic meter-reading method was proposed for gas meter reading [20]. The proposed method [20] is composed of three steps: meter detection, digit segmentation, and number recognition. In the three steps, YOLOv3, maximally stable extremal regions (MSER), and a modified VGG network are adopted in [20]. In [22], a two-stage method is proposed for an automatic meter reading. In the first stage, a small model, called fast-YOLO, is used for counter-detection. After counter-detection, digit recognition can be achieved based on CR-NET, CRNN, and multi-task learning in the second stage. For classifying document images, some Mandarin Chinese words such as \" \" (Property Owner) and \" \" (Name of Applicant) can be used as the special information to distinguish Figure 1a from Figure 1b. This circumstance has inspired us to analyze the document images for finding the specific information via CNNs.