# Smart Project Management: Interactive Platform Using Natural Language Processing Technology
## 3 Applications for Scene Text Detection
This approach incorporates both the idea of the CTPN small-scale candidate box and the idea of the Single Shot MultiBox Detector (SSD) approach, and achieves the effect of state-of-art of text detection in natural scenes. Then one analyzes the network architecture of SegLink to further understand how SegLink can achieve efficient multi-angle text detection. The SegLink architecture adopts the idea of SSD. First, Visual Geometry Group (VGG) 16 is used as the feature extraction for the backbone. The fully connected layers (fc6, fc7) of VGG are replaced by convolutional layers (conv6, conv7). One connects the conv layers conv8 to conv11. It is worth noting that the size between conv4-conv11 decreases in turn (each layer is one-half of the previous layer). This approach is for multiscale target detection, that is, large feature maps are good at detecting small objects. By using multiple feature maps of different scales and detecting segments and links from six feature layers, text lines of different sizes can be detected. There is no mention of whether the approach can detect curved text [27]. The method of segmenting and integrating the complete text line first detects and then merges, which undoubtedly greatly increases the loss of text detection accuracy and time consumption. A study in 2017 proposed that efficient and accurate scene text can solve multi-angle text detection and is simple and powerful [25]. There are multiple stages of text detection; taking the region proposals detection approach as an example, it includes the stages of candidate box extraction, bounding box regression, and merging candidate box. There is (a) horizontal word detection and recognition pipeline proposed by [28], (b) multidirectional  
Figure 1: Illustration for Segment Linking (SegLink) processes [24]. text detection pipeline [29], (c) horizontal text detection using CTPN, and (d) Efficient and Accurate Scene Text (EAST) pipeline, which eliminates most of the intermediate steps, consists of only two stages, and is much simpler than previous solutions. The author of EAST believes that splitting a text detection approach into multiple phases does not actually have many benefits, and it is correct to implement a true end-to-end text detection network. Therefore, the EAST process is quite concise, and is only divided into the Fully Convolutional Network (FCN) generation text line parameter stage and the local perception Non-Maximum Suppression (NMS) stage [30], which has further improved the accuracy and speed of text detection. To understand the advantages of EAST for text detection from the network architecture, EAST's network architecture is divided into three parts: feature extraction layer, feature fusion layer, and output layer. Feature extraction layer: Backbone uses deep but lightweight neural networks (PVANet) for feature extraction [31], and then sends it to the convolution layer, and the size of the subsequent volume base layer decreases sequentially (the size is half of the previous layer), and the number of convolution kernels increases sequentially (for the previous layer double). Feature maps at different stages are extracted so that feature maps of different scales can be obtained. The purpose is to solve the problem of intense text line scale transformation. Large-size layers can be used to predict small text lines, and small-size layers can be used to predict large text. Feature merging layer: merge the extracted features. The merging rule adopts the U-net method [32]. The merging rule is to merge the top features from the feature extraction network downward according to the corresponding rules [27].