# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 4 Experimental Results and Analyses
### Scene Text Recognition  
Train the small sample dataset and the large sample dataset separately. The small sample dataset refers to the synthetic dataset generated using the Text Recognition Data Generator tool, while the large sample dataset is composed of both the synthetic dataset and the MJ dataset. The two types of datasets will be used separately to conduct text recognition experiments on both the proposed model and the classical model, using recognition accuracy as the performance evaluation metric for scene text recognition models. The impact of background noise and character noise on the accuracy of different scene datasets will be analyzed. (1) Synthetic scene dataset experiment  
The model presented in this research and the classical model are trained on the text-train dataset and tested on the text-test dataset, with results displayed in Table 2. When tested on a small sample dataset, this paper's model has an accuracy of 98%, and our suggested scene text recognition model, which eliminates the interference of backdrop and character noise, achieves a superior accuracy trade-off than other current models. Specifically, our suggested model outperforms attention-based integrated network for scene text recognition (ABINet), the scene text recognizer published in [19], by 7.42% on text-test. Overall, Acc obtains the maximum improvement of 16.28%, the lowest improvement of 4.2%, and an average increase of 10.41% when compared to other classical models. The experimental findings suggest that the model presented in this study may increase identification accuracy by reducing background and character noise in a synthetic scenario. (2) Experiments on real scene datasets  
Using text-train and the MJ dataset to train this model and the classical model, the model is tested on the actual scene datasets IIIT5K, IC15, IC03-Char, IC03-Word, and CUTE80, matching the findings shown in Table 3. The accuracy of this paper's model on the IIIT5K, IC15, IC03-Char, IC03-Word, and CUTE80 datasets is 96.10%, 80.47%, 87.22%, 83.75%, and 87.50%, respectively. It outperforms the ABINet scene text recognition technique by 9.65%, 5.78%, 4.89%, 0.02%, and 3.45%, respectively, thanks to the character feature augmentation effect provided by the ENBC network. Compared to other models, the recognition results of the proposed model in this paper show an improvement of at least 2.92%, 5.17%, and 2.56% on the IIIT5K, IC15, and IC03-Char datasets, respectively. However, the performance is slightly worse than the TrOCR model by 0.98% and 2.17% on the IC03-Word and CUTE80 datasets. Furthermore, our method outperforms CRNN [12], RobustScanner [63], SRN [64], STAR-Net [65], PREN [21], ViTSTR-Small [66], ABINet [19], TrOCR [67], and MASTER [68] with overall average accuracies of 72.22%, 79.03%, 80.75%, 78.79%, 81.62%, 79.62%, 82.25%, 83.53%, and 82.44% on public datasets, respectively, while \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Training Dataset** & **Text-Test** \\ \hline CRNN & Text-train & 82.91 \\ RobustScanner & Text-train & 88.28 \\ SRN & Text-train & 87.67 \\ STAR-Net & Text-train & 85.75 \\ PREN & Text-train & 86.04 \\ ViTSTR-Small & Text-train & 81.72 \\ ABINet & Text-train & 90.58 \\ TrOCR & Text-train & 93.80 \\ MASTER & Text-train & 91.57 \\ Ours & Text-train & 98.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of synthetic scene dataset (%). our model's total average rate can reach 87%. The experimental findings suggest that the model proposed in this study may increase recognition accuracy by removing background and character noise in the real picture. (3) Trade-off study During the experiments, we conducted a comprehensive trade-off study on the average accuracy (Avg), parameters (Parameters), computational complexity (FLOPs), and inference speed (Speed). Table 4 shows the overall trade-offs under three comparison dimensions (accuracy vs. parameters, accuracy vs. FLOPs, accuracy vs. speed).