## 5 Conclusions  
We proposed an end-to-end trainable neural network model, SwFB, capable of directly converting document images into key field text, significantly enhancing the efficiency of personnel in the logistics industry. Traditional visual text extraction methods typically involve \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(P\) & \(R\) & \(F1\) \\ \hline donut & 82.5 & 81.2 & 81.8 \\ dessert & 85.8 & 83.6 & 84.7 \\ DocParser & 88.5 & 86.2 & 87.3 \\
**SwFB (ours)** & **87.1** & **85.3** & **86.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of other end-to-end methods on SROIE. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **OCR** & **F1** & \(BTED\) \\ \hline BERT & ✓ & 73.0 & 65.5 \\ BROS & ✓ & 74.7 & 70.0 \\ LayoutLMv2 & ✓ & 78.9 & 82.4 \\ donut & & 84.1 & 90.9 \\ DocParser & & 84.5 & 91.2 \\
**SwFB (ours)** & & **88.4** & **93.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of other methods on CORD. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & \(P\) & \(R\) & \(F1\) \\ \hline
**Co-G-Ma (ours)** & **78.5** & **65.7** & **72.4** \\ -CNN & 74.7 & 62.7 & 68.2 \\ -GRU & 74.8 & 65.4 & 69.7 \\ -CNN-Ma & 72.6 & 66.2 & 69.3 \\ -GRU-Ma & 69.3 & 65.1 & 67.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of other modules with \(1/5\) samples of COFIE. three independent tasks: text detection, text recognition, and text extraction, with models trained separately for each task, leading to cumulative errors. In contrast, our proposed feature-enhanced end-to-end neural network model enables direct conversion from raw images to structured text information, thereby reducing errors introduced by intermediate steps and improving overall accuracy. SwFB utilizes the Swin Transformer as the visual encoder and BART as the language decoder. By integrating CNN, GRU, and multi-head attention, we construct the feature enhancement module Co-G-Ma. The incorporation of this module enriches the input to BART with more comprehensive layout features and context information. This improvement contributes to enhancing the accuracy of the decoder, which is particularly effective in scenarios with low image quality and complex layouts, such as courier order images. However, despite the model's good performance in text extraction tasks, it has certain limitations, such as potential degradation in performance when handling extremely low-quality images and complex background noise. Additionally, the model's reliance on a large amount of labeled data may restrict its generalization capabilities. Future research could focus on enhancing the model's robustness on low-quality images and complex backgrounds, exploring more effective data augmentation techniques, and expanding the model's applicability to improve its performance across various document types and multilingual environments. Conceptualization, W.S., H.L. and Y.J. ; methodology, C.Q.W. ; software, W.S., H.L. and Y.J. ; validation, W.S., H.L. and Y.J. ; formal analysis, W.S., H.L. and Y.J. ; investigation, W.S., H.L. and Y.J. ; writing--original draft preparation, W.S., H.L. and Y.J. ; writing--review and editing, C.Q.W. ; visualization, H.L. and Y.J. ; supervision, W.S. ; project administration, W.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Our research data comes from real postal services, and the data shown in this article does not involve the privacy of relevant personnel. Since names, addresses, and mobile phone numbers involve user privacy, we apologize that the real data from postal services cannot be made public. We sincerely express our gratitude to [PERSON] from Qianjiang Customs of the People's Republic of China, as well as [PERSON] and [PERSON] from China Post Group Co., Ltd. Hangzhou Branch, for providing the necessary data set for this research. Their contributions are invaluable, and their support has significantly enhanced the quality of our work.