# Advanced Dense Text Detection in Graded Examinations Leveraging Chinese Character Components
## 1 Introduction  
The task of detecting Chinese characters is not only an important step in Chinese character recognition and processing, but also widely used in the fields of cultural heritage preservation, calligraphy analysis, and font generation. However, due to the multi-scale characteristics, complex background, and dense distribution of Chinese character components, traditional image segmentation methods are difficult to meet the requirements of practical applications. The main challenges of handwritten Chinese character detection can be summarized as follows: first, the structure of Chinese characters is inherently complex, with eachglyph consisting of multiple strokes and complex relative positional relationships between the strokes. Some Chinese characters have large differences in stroke order, curvature, and structural combinations, making recognition more difficult. Compared with Latin letters or numbers, the structure of Chinese characters is not only more complex but also varied. Secondly, there are significant individual differences in the writing styles of handwritten Chinese characters. Factors such as writing styles, stroke thickness, and inclination of characters by different writers can lead to differences in the performance of the same character shape among different individuals. The issues of blurred handwriting, overlapping characters, and hyphenation, all caused by excessive writing speed, can significantly affect the accurate recognition of characters. Furthermore, in practical applications, background noise, texture interference, and other non-target elements often appear in handwritten Chinese character images, and these factors place higher demands on character localization and segmentation. Finally, in some low-quality handwritten texts, characters may overlap and occlude, which makes the boundaries between characters blurred and increases the difficulty of detection and segmentation. [PERSON] [1] and others proposed a text region detection method based on multi-scale sliding windows, which efficiently identifies text regions with good results by sliding classification of each window through a convolutional network. [PERSON] [2] proposed the CTPN (Connectionist Text Proposal Network) text detection network, which holds a significant position in the field of text detection, and is especially suitable for the detection task of scene text box narrow, because the size of the text box varies greatly. CTPN uses fixed text anchor frames of different sizes and combines them with the Faster-RCNN concept to efficiently detect small-size text regions. Meanwhile, CTPN uses a text line construction method to connect multiple candidate text boxes into complete text boxes. Subsequently, the network further processes the convolutional features through a bidirectional LSTM structure to enhance the representation of spatial as well as sequential information. On this basis, the EAST (Efficient and Accurate Scene Text Detector) algorithm proposed by [PERSON] [3] draws on the concept of U-Net [4], employing a fully convolutional neural network (FCN) and non-maximum suppression (NMS) algorithms to streamline intermediate processing steps, enabling efficient detection at the single-character level. SegLink [5] is an improved version of CTPN, which not only makes tilt improvement for text box prediction but also optimizes the positioning of text boxes by connecting candidate boxes through neural networks. PixelLink [6] predicts text pixels and non-text pixels separately by training a convolutional neural network model and connecting them for instance segmentation. Based on this, the TextSnake [7] algorithm handles irregular text regions more flexibly by predicting the position of the centerline of the text, the radius of the central disc, and the angle between the centers of adjacent discs, thus avoiding the dependence of traditional prediction frames (e.g., quadrangles and rotated rectangles) on the shape and length of the text.