# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 4 Experimental Results and Analyses
### Scene Text Recognition
For example, recognizing the proper example of \"American\" is tough since the font color and background color are quite close to the human eye, but this paper model can detect the true impact. When recognizing \"HOW\" in the correct example, there is a lot of noise in the character itself, but using the ENBC network in this study, it can accomplish the impact of denoising and improve recognition accuracy. Figure 12 also contains instances of recognition mistakes, which may be broken down into three categories. First, the curvature of the text is too great, as seen by the semi-ellipse shape in the \"ALLAHABAD\" picture, causing certain pixels to be true throughout the model identification process, impacting the right recognition results. Second, there are too many missing characters, such as the \"\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{ }}}}}}}}}}}}}}}}\) in the image, which was incorrectly identified as an \"\(\mathbf{n}\)\" due to a significant lack of the lower portion of the character. Third, major blurring and big exposure, such as \"adapter\" were misread as \"_daoier\", owing to the exposure rate being too strong, immediately lost the initial letter, and serious blurring caused by the \"pt\" was misclassified as \"oi\". These mistake instances also indicate the suggested model's future research directions. Finally, Figure 13 depicts some sample instances of the proposed model on the scene dataset, categorized based on the recognition effect, with  
Figure 11: Correct example process of scene image recognition. Figure 12: Example process of scene image recognition error. To summarize, the model presented in this research can successfully reduce background and character noise in natural scene photos while also improving identification performance when compared to the traditional model. (5) Adaptation Strategy  
Considering that the experiments in this paper require discussing adaptation strategies to provide a comprehensive evaluation, we adopted three domain-adaptation fine-tuning approaches in our ENBC model. First, the decoder (multi-level character feature fusion module) is responsible for merging multi-scale features extracted by the encoder with low-level details to eliminate background noise and generate the reconstructed image. Secondly, the recognition enhancement module improves the accuracy of character sequence prediction by modeling the context using a bidirectional transformer. Lastly, joint fine-tuning (decoder + recognition module) adjusts the parameters of both the decoder and the recognition module simultaneously, maximizing the domain adaptation effect. In the text-train + MJ pre-trained ENBC model, we used IC15 as the primary evaluation metric, and IIIT5K and CUTE80 as auxiliary metrics, to validate the model's generalization ability. Table 5 presents the design of the fine-tuning strategies (the explanation for the table header can be found in the table notes), and Table 6 shows the comparison of performance improvements. According to the data presented in Table 6, strategy B (fine-tuning only the recognition module) improved the accuracy of IC15 by 5.15%, validating the sensitivity of the recognition module to domain adaptation. Strategy C (joint fine-tuning) further improved the accuracy to 86.10%, with minimal impact on other datasets, indicating that global optimization is more effective. It is worth noting that the IC15 training set contains only 1000 images, so semi-supervised learning (using unlabeled real data) can effectively alleviate the issue of insufficient data and enhance the model's performance. Figure 13: Some sample examples.