# Smart Project Management: Interactive Platform Using Natural Language Processing Technology
## 3 Applications for Scene Text Detection  
Scene text detection is intuitively understood. Given an image, all the positions where the text appears in this image are needed, that is, find the position of each object in the image, and mark the objects in the bounding box because images are divided into two categories, with and without text, which is a single-class detection task. Faster Region-based Convolutional Recurrent Neural Network (RCNN) can be used for text detection [22; 23]. The Faster RCNN model is mainly composed of two modules: the Region Proposal Network (RPN) candidate frame extraction module and the Faster RCNN detection module, which can be subdivided into four parts: (1) Conv Layer, (2) RPN, (3) Roll Pooling, and (4) Classification and Regression. The steps of Faster RCNN are divided into [24]: (1) The basic network performs feature extraction, (2) features are sent to RPN for candidate frame extraction, (3) the classification layer classifies the objects in the candidate frame, and the regression layer fine-tunes the (x, y, w, h) of the candidate frame. However, the effect of Faster RCNN as text detection is not ideal due to the uniqueness of each text. For example, common objects have obvious closed edge contours, but the text does not. The text contains multiple texts, and there is text between the texts. If the interval between texts cannot be detected, each character is treated as a text line and it is framed instead of the entire line. Based on the said reasons, general networks such as Faster RCNN must be improved to design a new network framework suitable for text detection. A work regarding detecting text in Natural Image was proposed [24]. This deep neural network is called Connectionist Text Proposal Network (CTPN), which can accurately locate lines of text in natural images. CTPN detects text lines directly in a series of fine-grained text proposals in convolutional feature correspondence. In this paper, a vertical anchor mechanism was developed to jointly predict the position and text/non-text score of each fixed-width proposal, greatly improving. For positioning accuracy, the sequence isproposed to be naturally connected through a loop neural network, which is integrated into the convolutional network to form an end-to-end trainable model. This allows CTPN to explore rich image context information and detect blurry text. CTPN works reliably on multiple scales and multiple languages without further follow-up processing. It departs from the previous method that required multiple steps of filtering from the bottom up. However, CTPN has an obvious disadvantage. It is not good for non-horizontal text detection. In this paper, the text in the detection result picture is horizontal [25; 26]. In order to solve the shortcomings of poor performance of CTPN in non-horizontal detection, a study introduced a detection approach that can detect text at any angle, which is generally called, in this approach, Segment Linking (SegLink) [24]. The main idea is to decompose the text into two locally detectable elements, that is, segments and links. For example, according to Figure 1, the first picture is a box of segments detected in the picture; the second picture is a line with links detected between adjacent segments; the third picture merges the whole words into segments connected by links [24]. The characteristic of text detection is that the aspect ratio is particularly large or small, and there is usually a rotation angle. If four parameters (x, y, w, h) of target detection were used to specify a target position, the error obtained will obviously be too big, so, then, one lets the model learn another parameter \(\theta\), and this \(\theta\) represents the rotation angle of the text box. The parameters returned from the original (x, y, w, h) to (x, y, w, h, \(\theta\)), the error in solving the angle occurs.