# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 3 Proposed Framework
### Pre-Processing  
This study generates the dataset needed for the ENBC network using the Baidu Flying Paddle Text Recognition Data Generator tool. The bulk of text in natural contexts is limited to a small number of computer-generated fonts and is created by physical rendering and imaging processes that are not governed by computer algorithms [53]. The Text Recognition Data Generator is a synthetic word data generator that mimics text picture scene distribution.  
Three distinct picture layers--a background image layer, a foreground image layer, and an optional border/shadow image layer--are created throughout the dataset production process and are represented by images with alpha channels. Figure 3 shows the creation procedure as well as a few examples of the synthetic data. Following font rendering, image layer creation and shading, projection distortion application, and image blending, Figure 3a illustrates the text generation process. Figure 3b depicts some drawn-at-random data generated by the Synthesized Text Tool. The synthetic data-generating procedure is as follows:  
1. Font rendering--Choose a typeface at random from the Google Fonts download's collection of more than 1400 fonts. Word counts, margins, underlining, and other characteristics are randomly distributed using a distribution that may be set in any way. The word is produced using either horizontal bottom text lines or random curves on the alpha channel of the foreground picture layer.  
2. Border/shadow rendering--The foreground rendering allows for the insertion of a border, start border, or shadow with any width.  
3. Base color filling--A distinct, homogeneous color is sampled from clusters on the original image to fill each of the three image layers. The RGB elements of every picture in the training dataset are divided into three groups to form these clusters.  
4. Distortion of projection--To imitate the 3D environment, random complete projection modifications are used on the foreground and border/shadow picture layers.  
5. Text image--Randomly selected picture segments from the SVT and ICDAR 2003 training datasets are mixed into each image layer. A stochastic process determines the quantity of blending and alpha blending modes (normal, add, multiply, burn, max, etc.), producing a diverse array of textures and compositions. To create a single text picture, the three image layers are also randomly mixed together.  
6. Noise--The image is subjected to JPEG compression artifacts, blurring, resampling noise, and Gaussian noise.  
Through the use of several random distributions that mimic actual text pictures of scenes, this approach creates a vast array of synthetic data samples. Real-world data are replaced with synthetic data, and tags are created as needed from a corpus or dictionary. Deep learning methods that have a high demand for data can be used to train richer  
Figure 3: Process of creating synthetic data. (**a**) The process of creating text; (**b**) a little data picked at random.  
whole-word-based models by providing an order of magnitude bigger training dataset than ever before.