# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 4 Experimental Results and Analyses
### Experimental Setup  
(1) Experimental environment: In the experimental verification, the computer hardware environment is Intel(R)Core(TM)[EMAIL_ADDRESS] GHz CPU, 16 GB RAM, NVIDIA GeForce GTX 2080 GPU, Nvidia, Santa Clara, CA, USA; the software environment is Windows 10 operating system; and the operation environment is Python 3.7, Pytorch 1.9, PaddleOcr 2.6, and CUDA Toolkit 10.2. Pytorch 1.9, PaddleOcr 2.6, and CUDA Toolkit 10.2 are used.  
(2) Training details: By examining the influence of text picture size on the model, the input image's height and width are 150 and 500, respectively. BCN consists of four levels, each with eight attention heads. The balance factors \(\lambda_{\
u}\), \(\lambda_{l}\) are set to one and one. The ADAM optimiser is used, with an initial learning rate of \(1\times 10^{-4}\) that decreases to \(1\times 10^{-5}\) after 6 epochs, a batch_size of 64, and an epoch_num size of 100.  
(3) Evaluation metrics: The ENBC network's performance is evaluated at the pixel level using output pictures. To statistically measure the network's denoising performance, we use accuracy, recall, F-score, and intersection over union (IoU). Set the foreground text pixels to positive (pixel value 0) and the background pixels to negative (pixel value 255). Compare the pixels in the output image to the pixels at the same position in the label image. They can be classified as true positive (TP) if they match the foreground text pixels, false positive (FP) if they differ, true negative (TN) if they match the background text pixels, and false negative (FN) if they differ. Based on these four categories, the model may be evaluated using three metrics:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{14}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{15}\]  
The F-score is the harmonic average of accuracy and recall:  
\[F-score=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{16}\]  
Recognition accuracy is a key performance statistic for assessing algorithms in scene recognition applications. Word accuracy (WA) is commonly employed as a word-level accuracy parameter in English scene text recognition to assess model performance. WA is the percentage of successfully identified characters to total test pictures. In this experiment, we run text recognition experiments on this model and the classical model on small and large sample datasets, respectively, and analyze the effects of character noise and background noise on recognition accuracy on different scene datasets, using recognition accuracy (Acc) as the model's performance evaluation metric.  
\[Acc=\frac{Number_{correct}}{Number_{total}}\times 100\% \tag{17}\]  
The ideal model best_accuracy on the evaluation set is chosen as the experimental model, and the experimental results in the synthetic scene dataset are as follows: The recognition improvement module properly recognizes 11,025 of the 11,250 photos in the test set, with a recognition accuracy (Acc) of 98% and an average time consumption of 0.038 s per image, meeting the real-time requirement of usage.