# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 1 Introduction
### A Two-Dimensional Attention-Based Method for Scene Text Recognition  
Overall, this text recognition approach converts the original one-dimensional predictive model to a two-dimensional (2D) version by creating a 2D decoder based on the attention mechanism [15; 16; 17], allowing the sequence model to use all geometric and character structure information for character feature alignment. Furthermore, there is an increasing tendency of merging visual and verbal data to improve recognition. For instance, [PERSON] et al. [18] used dictionaries as linguistic information to improve initial recognition results, ref. [19] proposed an iterative linguistic structure to correct initial recognition results, and [PERSON] [20] et al. proposed a hybrid bag of features-stacked autoencoder-hashing attention model (BoF-SAE-HAM) design for recognizing words in natural scenes. In addition, investigating improved character representations is a promising avenue for scene word recognition. [PERSON]. [21] sought to create basic representations on undirected graphs and utilize graph convolutional network (GCN) to improve character recognition. In the last several years, deep learning techniques based on semantic segmentation have steadily been used to STR. [PERSON]. [15] created a fully convolutional network based on the character attention mechanism to recognize scene text and forecast characters' exact locations. Although the approaches mentioned above have had some success, the current STR methods are only relevant to standard English typefaces and a few categories of numerical identification, and the recognition accuracy is low. Combining the aforementioned issues, this study provides a scene text recognition approach to identify large-scale characters using pixel-by-pixel prediction from a two-dimensional viewpoint, and applies it to several irregular English and numeric characters, as illustrated in Figure 1c. Background noise and character noise are frequently regarded as worthless and impeding information for text recognition. To address the aforementioned issues, an STR model called the eliminating noise from background and character network (ENBC) is proposed. The model is made up of three modules: high-level character feature extraction, multi-level character feature fusion, and recognition improvement. In the feature encoding stage, the scene image is convolved with ASPP voids in the high-level character feature extraction module to capture the character feature information and eliminate the noise interference of the characters. In the decoding and reconstruction stage, the image is fused with different levels of character feature information through the multilevel character feature fusion module to eliminate the background noise, and then the reconstructed image is The reconstructed picture is then merged with cross entropy and an auxiliary loss function to indefinitely approximate the label image; finally, the reconstructed picture is upgraded by the recognition enhancement module to increase character context modelling capacity and text recognition accuracy, and the method's reliability and efficacy are tested using tests. This publication contributes the following:1. A new scene text recognition method is proposed to eliminate background and character noise interference. The method uses the lightweight MobileNetV2 as the base network and employs ASPP dilated convolutions with different dilation rates to capture features at different scales, thereby expanding the receptive field. This enables more effective capturing of character feature regions, eliminating character noise interference, and enhancing character shape features. By combining the high-level character feature information obtained through sampling with the low-level character information in the backbone network, the foreground characters and background interference are separated, and background noise is eliminated. 2. A recognition improvement module is designed to learn text basic representation.