# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 1 Introduction  
Scene text recognition (STR) is an emerging study topic in computer vision, with applications including robotics process automation, picture retrieval, autonomous driving, real-time translation, and more. STR is the mechanical extraction of text information from pictures or movies in realistic situations, and it is more complex than optical character recognition (OCR) operations [1, 2]. This is because the text in images of natural scenes has multiple languages, rich font styles, diverse colors, variable scales, and free arrangements, among other issues. Meanwhile, the backgrounds in natural scene images are complex and variable with serious noise interference, and some background objects and texts are actually difficult to distinguish.  
As a response to the aforementioned challenges, a variety of text recognition technologies are presently presented, which may be classed as classical or deep learning approaches based on the manner of feature extraction. In well-known conventional approaches, character pictures are often represented using manual characteristics [3, 4, 5]. To extract characterpicture feature information, approaches such as co-occurrence histogram of oriented gradients (co-HOG), histogram of oriented gradients (HOG), and convolutional co-occurrence histogram of oriented gradients (ConvCo-HOG) were utilized. For example, [PERSON] et al. [5] investigated an effective extraction approach by converting picture information into consecutive column vectors using HOG algorithms. [PERSON] et al. [6] recognized printed text using iterative and heuristic techniques; [PERSON] et al. [7] utilized the K-mean approach to coarsely classify the characters and retrieved the character feature information in eight directions and then finely classed the character feature information using the improved minimum quality discriminant function (MQDF) classifier; [PERSON] et al. [8] presented the adaptive discriminative localization alignment (ADLA) approach for similar character detection based on character similarity to address the annoyance created by similar characters in pictures. However, traditional methods of recognizing text are limited to the artificial design of feature expression ability and processing complexity. It is challenging to attain higher identification accuracy in increasingly complicated visual scenarios, such as text images with significant angular distortion and blurring.  
As deep learning technology advances rapidly in the field of text recognition, text feature extraction methods have gradually evolved from manual design to automatic extraction using deep neural network models, resulting in optimized recognition performance and significantly improved the accuracy. Deep learning-based text recognition systems typically involve four major steps: preprocessing, feature extraction, sequence modeling, and prediction. During the preprocessing stage, the input picture is denoised, greyscaled, binarized, and scaled to increase recognition accuracy and efficiency. Then, in the feature extraction step, from the preprocessed image, a deep convolutional neural network (CNN) is applied to extract local visual information, which our technique primarily improves upon to tackle the problem effectively from the root. The long-range relationships between distinct characteristics are explored further during the sequence modeling step. Finally, in the prediction step, prediction results for text occurrences are generated [9]. In this way, STR systems may be classified into two types depending on the application dimension of the attention mechanism: attention that is both one- and two-dimensional. Figure 1 depicts the picture character recognition method from both one-dimensional and two-dimensional perspectives.