# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 2 Related Work
### Scene Text Recognition Method Based on Image Segmentation  
Segmentation networks must have excellent modeling capabilities in order to make correct judgments regarding text pixels, background pixels, and the transition zone between them; otherwise, mis-segmentation such as over-segmentation, missing, blurring, and so on may occur. The aforementioned problems are somewhat alleviated by the scene text segmentation methods now in use, but there is still room for improvement. Spatial and multi-scale attention network (SMANet) [36] employs a pooling technique to extract multiple scale text characteristics; however, it loses spatial location information for scene text and is unable to maintain feature map resolution. Multi-granularity network (MGNet) [37] uses an instruction under partial supervision technique with text polygonal box mask labels to offer a priori knowledge for pixel-level text segmentation, which aids in determining text position; nevertheless, it lacks a network architecture for natural scene text features. Deep convolutional neural network with transfer learning (Deep-CNNTL) [38] combines deep convolutional neural network (DCNN) with TL to enhance text boundary detection; however, the approach still has a lot of space for improvement when dealing with excessively noisy scene photos. Text recognition network (TexFNet) [39] employs the same strategy in the generic segmentation backbone network [40; 41; 42; 43] after developing an improved network that leverages cosine similarity to repair misclassified text pixels with unusual textures and styles. TexFNet is customized based on the scene text properties, which can lead to improved segmentation results. The potential for improving generic segmentation networks in scene text segmentation tasks is proven, although the discriminator in this technique must be trained using complicated character-level annotations.  
Semantic segmentation, in contrast to ordinary segmentation, categorizes each pixel in a picture. Semantic segmentation [40; 44; 45; 46; 47] is a common computer vision problem for identifying the category, position, and form of items in a picture. The fully convolutional network (FCN) [48] framework design has been used to extend most semantic slicing techniques, including semantic image segmentation with deep convolutional Nets, atrous convolution, and fully connected CRFs (DeepLab) [49], convolutional networks for biomedical image segmentation (U-Net) [50], pyramid scene parsing network (PSPNet) [51], and others. Refs. [15; 52] successfully employed semantic segmentation models to fulfill the STR challenge. However, they can only detect text in basic scenarios. [PERSON] et al. [52] also introduced a segment anything model (SAM) to the mask an end-to-end trainable scene text spotting system (TextSpotter) [52] model to address the multilingual end-to-end recognition challenge. However, when detecting text in complicated contexts, the character segmentation branch is muted, and sequence prediction is handled only by the SAM [52], resulting in some recognition restrictions. The work in this study is based on a traditional semantic segmentation framework and the design of an ENBC network to extract features for pixel-by-pixel classification, with the purpose of reducing background noise with the interference of the character's own noise, enhancing the character's shape features, and improving text recognition accuracy.