# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 4 Experimental Results and Analyses
### Scene Text Recognition
Our model achieves an average accuracy of 87.00% with a speed of 38 milliseconds per image, having 39.7 M parameters and a computational complexity of 8.65 \(\times\) 10\({}^{9}\) FLOPs. Specifically, compared to MASTER, our model has 4.55 \(\times\) 10\({}^{9}\) fewer FLOPs, with 11.7 M more parameters and 31.55 milliseconds slower speed per image, but it outperforms the MASTER model by 4.56% in terms of average accuracy. As for the parameters of our model, it can be seen from the table that compared to the STAR-Net model, it reduces 9.2 M parameters, and compared to the SRN model, it reduces 17.6 M parameters. Additionally, the parameters, computational complexity, and inference speed of our model are significantly better than those of TrOCR, and its average accuracy also outperforms other models. Therefore, the proposed ENBC model is at or near the optimal point in the trade-offs between accuracy vs. speed, accuracy vs. parameters, and accuracy vs. FLOPs, with shorter runtime and lower computational overhead. (4) Model failure analysis Figures 11 and 12 demonstrate several accurate and incorrect recognition instances from this paper's model on the scene dataset. Among them, the first row of the table is the input image, the second row is the reconstructed image through the ENBC network to \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Avg** & **Parameters (\(1\times 10^{6}\))** & **Flops (\(1\times 10^{9}\))** & **Speed (ms/Image)** \\ \hline CRNN & 72.22 & 8.5 & 1.4 & 3.7 \\ RobustScanner & 79.03 & 20.6 & 5.0 & 16.8 \\ SRN & 80.75 & 57.3 & 10.8 & 18.8 \\ STAR-Net & 78.79 & 48.9 & 10.7 & 8.8 \\ PREN & 81.62 & 20.0 & 3.45 & 29.5 \\ ViTSTR-Small & 79.62 & 21.5 & 4.6 & 9.5 \\ ABINet & 82.25 & 36.7 & 8.3 & 33.9 \\ TROCR & 83.53 & 558.0 & 15.2 & 318.0 \\ MASTER & 82.44 & 28.0 & 13.2 & 6.45 \\ Ours & 87.00 & 39.7 & 8.65 & 38.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model average accuracy (Avg. ), parameters, computational requirements (FLOPs), and speed. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Training Dataset**} & \multicolumn{6}{c}{**Test Dataset**} \\ \cline{3-8}  & & **IIIT5K** & **IC15** & **IC03-Char** & **IC03-Word** & **CUTE80** & **Avg** \\ \hline CRNN & Text-train+MJ & 79.38 & 61.35 & 72.22 & 76.31 & 71.87 & 72.22 \\ RobustScanner & Text-train+MJ & 83.16 & 72.1 & 77.96 & 81.46 & 80.49 & 79.03 \\ SRN & Text-train+MJ & 83.52 & 72.82 & 83.52 & 81.52 & 82.37 & 80.75 \\ STAR-Net & Text-train+MJ & 85.20 & 74.50 & 83.05 & 82.00 & 69.20 & 78.79 \\ PREN & Text-train+MJ & 87.05 & 73.45 & 81.38 & 80.61 & 85.61 & 81.62 \\ ViTSTR-Small & Text-train+MJ & 85.60 & 75.30 & 83.10 & 82.80 & 71.30 & 79.62 \\ ABINet & Text-train+MJ & 86.45 & 74.69 & 82.33 & 83.73 & 84.05 & 82.25 \\ TROCR & Text-train+MJ & 84.35 & 74.22 & 84.66 & 84.73 & 89.67 & 83.53 \\ MASTER & Text-train+MJ & 93.18 & 70.52 & 83.76 & 82.67 & 82.07 & 82.44 \\ Ours & Text-train+MJ & 96.10 & 80.47 & 87.22 & 83.75 & 87.50 & 87.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of real scene dataset (%). enhance the character features, the third row is the real result of the recognition expectation, and the fourth row is the recognition result through the model proposed in this paper. Furthermore, the black type in the table's recognition results represents successfully identified characters, the red font indicates erroneously recognized characters, and the \" -\" indicates loss of recognized characters. The model in this research performs admirably in demanding text pictures, as seen by the accurate recognition examples in Figure 11. Specifically, the model in this paper excels at recognizing scene text images with character textures similar to the background texture, complex backgrounds, and strong noise interference from the characters themselves, as well as some scene text images that are difficult to recognize even by humans.