# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 2 Related Work  
### Text Recognition of Scenes Using Deep Learning  
General optical character recognition approaches are unable to effectively address the varied backgrounds, different character sets, uneven lighting, and poor resolution that characterize scene character recognition. The early approaches in STR mainly depended on hand-crafted features to overcome the difficulties. When extracting features from photos with intricate backgrounds and characters, the HOG approach is employed [22; 23] presented two new feature descriptors, ConvCo-HOG and Co-HOG, which are based on the HOG approach. In order to conduct word recognition without character segmentation, [PERSON] et al. [24] employed HOG to transform word pictures into sequence features, which they then fed into LSTM and CTC. Even while these techniques work well, they have drawbacks, such as the fact that they are often intended for particular scenarios.  
In recent years, to address various objectives, researchers have attempted to enhance feature extraction networks in recent years from several angles. Recurrent convolutional neural network [25] proved well in picture classification tests, and [PERSON] et al. [26] used this technique to extract features from a natural scene text recognition network. In order to further optimize the information modeling process, they also devised a gating mechanism. [PERSON] et al. [27] presented a novel method for automatically recognizing and categorizing handwritten digits by utilizing the DenseNet-41 framework in the feature extraction stage in conjunction with the faster kernelized convolutional neural network (Faster-KCNN) deep learning algorithm. A continuous convolutional activation (CCA) technique was developed by [PERSON] et al. [28] that integrates both high-level and low-level patterns into a single feature vector. Usually, these algorithms generally transform a 2D image into a 1D feature vector. As a result, they produce noise in the background and have no ability to localize character positions. Therefore, in order to get the network to concentrate on retrieving foreground text information, several researchers have coupled convolutional processes with attention mechanisms [29]. [PERSON] et al. [30] suggested using a binary strategy to enhance the feature extraction convolutional network to comply with the requirement for real-time recognition of text from natural scenes. This approach can minimize memory occupation and speed up inference. To achieve optimal performance, the feature extraction network must undergo extensive and time-consuming parameter tweaking on various datasets.  
Scene texts have a wide and complicated structure. As a result, several researchers have created generative tasks [31; 32; 33] to identify distinctive characteristics. [PERSON] et al. [34] devised a way to learn the canonical forms of glyphs in many popular font styles using a generative adversarial network (GAN). The framework is made up of four networks: feature extraction, character categorization, character production, and character discrimination.  
Ref. [35] suggested multi-task collaborative generative adversarial network (MC-GAN), a network architecture for producing actual data, to replicate real-world datasets and increase STR accuracy. However, both [34; 35] rely on uncontrolled font renderers to create standard print characters, which is extremely limited for recognition.