## 1 Introduction  
In recent years, cross-border logistics has experienced rapid growth. Involving multiple countries and regions, cross-border logistics has longer transportation times and more transit stations. The transportation processes of parcels include international shipping, customs clearance, and other processes. The information entry of parcels is a crucial component in these processes. In the past, manual data entry was the only solution. Technological advancements in computer science have introduced new solutions to the problem. Leveraging artificial intelligence technology can enhance automation, reduce manual intervention, and better adapt to the rapidly evolving demands of e-commerce. Currently, information extraction from courier order images faces multiple challenges. Firstly, due to privacy concerns, there is a lack of publicly available real courier order image datasets. Secondly, compared to other document images, such as receipts and train tickets, courier order images have lower quality and a more complex layout. Thirdly, the text within courier orders does not follow a standardized writing format, making the capture of semantic information more challenging. Due to these challenges, at present, cross-border waybill information must be manually extracted and entered into a post system for subsequent processing, as typically performed in many countries, including China. While there is no specific research on text extraction from courier order images, researchers have explored various Visual Document Understanding (VDU) tasks on other document images, focusing on three main categories: image classification [1], text parsing [2], and knowledge question-answering [3]. Most VDU systems [4; 5; 6; 7] rely on Optical Character Recognition (OCR), which involve labeling OCR outputs and building Seq2 Seq models to complete the entire task. For these studies, the entire task is divided into three components: text detection, text recognition, and text extraction, as illustrated in Figure 1. In such systems, the neural network models for these three tasks are trained independently, leading to error accumulation [8; 9]. We utilized two publicly available OCR models (Paddle OCR and Tesseract OCR) for text recognition, but both exhibited poor accuracy on courier order images. Additionally, OCR results only provide a simple conversion of individual words, containing some redundant information, requiring additional processing for effective information entry. For the entry of courier order information, we propose a solution building upon prior work [10; 11; 12]. Our proposed method is an end-to-end neural network model based on feature enhancement named SwFB, and we construct our feature enhancement module, Co-G-Ma, based on CNN [13], GRU [14], and multi-head attention [15]. SwFB utilizes a Swin Transformer as the visual encoder and BART as the language decoder, enabling the transformation from raw images to structured text information. In this architecture, the visual encoder is responsible for extracting features from the original RGB images, generating word feature vectors. These vectors pass through a feature enhancement module to acquire more intricate layout features and contextual information. Finally, the language decoder transforms these features into sequences with special tokens. We design decoding rules for the decoder to generate the sequence-to-structured text information transformation, completing the entire task. We have conducted a comprehensive literature review across multiple academic platforms. However, due to the unique characteristics of such a dataset, we found a notable lack of research on the extraction of visual text from cross-border logistics waybill images. Overall, our contributions can be summarized as follows:  
* We collected a total of 496 images of various categories of cross-border logistics courier orders, and we constructed a dataset named COFIE for courier order image information extraction by annotating key information in the recipient address area with JSON files.