# Advanced Dense Text Detection in Graded Examinations Leveraging Chinese Character Components
## 2 Components-ECA-EAST Network  
### 2.1. CEE Network Structure  
In the research of detection and segmentation of handwritten Chinese character images, the EAST model has been widely adopted due to its efficient and accurate text detection capabilities [11]. The EAST model proposes a simplified end-to-end text detection method, which is especially suitable for dealing with scene text detection tasks. Unlike traditional methods, EAST does not rely on Region Proposal Networks (RPNs) or candidate frame generation but avoids complex processing by predicting the bounding boxes and geometric properties of text regions directly.  
Specifically, EAST utilizes a Fully Convolutional Neural Network (FCN) for image feature extraction, predicting whether each pixel belongs to a text region while simultaneously inferring morphological features of the text, such as its orientation and size [12]. This method significantly improves the speed of text detection while maintaining high accuracy in diverse background environments.  
A major advantage of the EAST model is its good adaptability. Many traditional text detection algorithms need to be specially adapted for different text scales and orientations, whereas EAST can flexibly cope with various types of text through a unified network architecture, especially when facing handwritten Chinese character images, it can effectively deal with a variety of irregular writing and complex backgrounds, and improve the robustness of detection [13]. Therefore, the adoption of EAST in the detection and segmentation of handwritten Chinese character images can significantly improve the accuracy, reduce the occurrence of false and missed detections, and provide more reliable input data for the subsequent text recognition and image segmentation tasks. However, the traditional EAST often suffers from the problems of missed detection and edge blurring when dealing with ultra-small targets and especially performs poorly in the detection of tiny parts in complex word classes [14], moreover, the performance of the EAST model is limited for the detection of dense and long texts. This also becomes an improvement idea for this paper.  
The improvements in this paper mainly focus on the feature extraction phase and feature fusion phase of EAST. To address the problem that EAST overly relies on the backbone network in the feature extraction phase, and insufficient processing of dense textual information, which leads to inaccurate feature extraction, information loss, and insufficient feature expression ability in complex backgrounds or fuzzy or overlapping Chinese character scenarios, this paper introduces the ECA attention mechanism in the backbone network to strengthen the capture of key information and the allocation of feature weights among channels [15]. Given the leakage and edge-blurring phenomenon of EAST in detecting small targets, this paper uses the self-built small parts dataset to extract the convolutional features and fuses them with the convolutional layer in the feature extraction stage through the feature pyramid network (FPN) further enhances the detection of small targets and the representation of boundary features. The CEE network structure is illustrated in Figure 1, where f, P, and h represent the feature maps obtained after the convolution operations.