# FAS-Res2 net: An Improved Res2 net-Based Script Identification Method for Natural Scenes
## 1 Introduction  
Identifying the script is a crucial aspect of scene text recognition and serves as a prerequisite step for text recognition. The goal is not to determine the specific text content but rather to identify the language of the text, enabling the selection of the appropriate text recognition model. Therefore, script identification can be viewed as an image classification problem, which has been thoroughly researched by an increasing number of experts over the years. Different script languages include Chinese, English, Arabic, Greek, etc. As shown in Figure 1, different character stroke structures from different script languages can be seen. Wide applications of script identification include online archiving of multilingual scene images, product image search, multilingual machine translation, scene image understanding, etc. Existing approaches for multi-script documents have demonstrated promising outcomes in script identification for printed, handwritten, or mixed documents [1]. However, integrating text recognition information into script identification, as proposed in [2], can be challenging, since extracting text recognition information is typically difficult. In [3], researchers utilized the VGG16 convolutional neural network to extract feature information, along with fine-grained and attention modules to identify fine-grained features at crucial positions. Ref. [4] provides a comprehensive overview of recent research progress in this field, highlighting the valuable achievements made in recent years. While the aforementioned methods have delivered satisfactory results, they primarily focus on script identification for documents. The extensive research and application of script identification is not only satisfied with document content, but script identification in natural scenarios is also becoming more and more important. Scene text images are different from printed document images, which is a big challenge in identification, mainly for the following reasons:  
1. Text and image styles in natural scenes are changeable. They can come from various constructions, such as outdoor billboards, traffic signs, user instructions, etc. Text images contain different fonts, colors, multiple artistic styles, and there are great variations. 2. Low resolution, noise, and light changes will cause distortion, reduce image quality, and affect the accuracy of identification. Since it is difficult to get rid of the influence of objective factors such as weather, brightness, and equipment when capturing images, the image quality needs to be improved. 3. Languages belonging to the same family may have minimal differences, and distinct languages may share a common subset of characters. For instance, Greek, English, and Russian share a set of characters, and the arrangement of these characters is nearly identical. However, accurately distinguishing between them requires identifying the unique characters and components specific to each language, which presents a fine-grained classification challenge. 4. Background interference has a direct impact on identification accuracy. When the background of the image overlaps with the text, the background may be mistakenly regarded as part of the text, thereby identifying the wrong script. The main work results are as follows:  
1. This paper proposes an improved scene script identification method based on convolutional neural network Res2 Net, namely FAS-Res2 net. 2. Feature Pyramid Network (FPN) was proposed to preserve the deep semantic feature information and shallow geometric feature information of text images. 3. An adaptive spatial feature fusion module is proposed to calculate the spatial weights of feature maps at different levels, and the weight fusion feature information solves the feature conflict between positive and negative samples. 4.