摘要： 针对物流证照图像中字段布局差异明显、相似字段易混淆、拍摄质量不稳定，以及现有生成式视觉语言模型在图像块一维序列化后二维空间关系表达不足、纯文本输出缺乏区域约束等问题，提出一种融合解耦二维位置编码、坐标监督提取和多任务指令混合的物流证照信息提取方法。该方法在下采样后的二维视觉特征映射至语言模型嵌入空间之前，引入行、列方向相互独立的可学习位置编码。通过分别维护行、列位置参数并按通道拼接形成二维位置先验，以较低参数开销补充视觉特征中的行列关系、局部邻接和版式结构信息，缓解图像块展平造成的空间拓扑弱化问题，增强模型对长条形文本、密集字段和相邻区域的区分能力。在信息提取阶段，将传统纯文本问答重构为字段文本与区域坐标联合生成任务，使模型以结构化序列同时输出字段内容及其归一化边界框，从而建立字段语义与图像区域之间的显式对应关系，提高提取结果的空间可溯源性和可验证性。针对直接引入坐标监督后文本生成与坐标预测之间可能出现的优化冲突，进一步构建联合提取、视觉定位和纯文本提取三类指令任务，并按照设定比例进行随机混合训练，使模型在学习空间定位能力的同时保持稳定的文本识别与指令理解能力。构建包含驾驶证、从业资格证、道路运输证和行驶证图像共4000张的物流证照数据集，数据覆盖多种版式和拍摄条件，并具有字段密集、文本区域细长、相似字段相邻及印章遮挡等特点。针对关键字段标注文本内容及其对应边界框，并转换为文本提取、视觉定位和文本—坐标联合提取指令样本。进一步引入DocVQA、XFUND-ZH、LSVT和LLaVA-Instruct-ZH等公开数据进行混合训练，以增强模型对通用文档结构、中文排版和复杂视觉干扰的适应能力。实验以InternVL2.5-2B为主要基座，并在InternVL3-2B和InternVL3-8B上开展扩展验证，采用平均归一化编辑距离相似度（ANLS）、完全匹配率（EM）、平均交并比（Mean IoU）和坐标感知准确率（Grounded Accuracy）评价文本识别、区域定位及文本—坐标一致性。实验结果表明，所提方法在保持较高文本识别性能的同时能够明显改善字段定位效果。与InternVL2.5-2B基线相比，四类物流证照上的ANLS和Mean IoU宏平均分别提高3.11和11.23个百分点，其中道路运输证的Mean IoU由13.42%提高至48.39%，Grounded Accuracy由17.32%提高至62.09%；在驾驶证数据集上，ANLS和Grounded Accuracy分别达到98.00%和48.97%。在InternVL3-8B上的3次独立运行结果显示，所提方法在四类数据集上的ANLS、EM、Mean IoU和Grounded Accuracy宏平均分别提高0.31、1.00、1.28和1.46个百分点，且标准差与基线处于相近范围，表明该方法在更大参数规模下仍具有稳定增益。消融实验表明，直接采用坐标监督难以有效兼顾文本生成与空间定位；在此基础上分别引入多任务指令混合和解耦二维位置编码后，Mean IoU与Grounded Accuracy均明显提升，二者联合使用时取得最佳综合性能。XFUND-ZH和DocVQA上的评估结果进一步表明，该方法在增强结构化文档空间建模能力的同时，未削弱通用文档问答能力。研究结果验证了显式二维空间先验与坐标监督协同建模对复杂物流证照信息提取的有效性，可为真实业务场景下的结构化文档理解与可追溯信息抽取提供技术支撑。

Abstract: To address large layout variations, confusion between similar fields, unstable image quality, weak representation of two-dimensional spatial relations after one-dimensional serialization of image patches, and the lack of region constraints in text-only outputs, this paper proposes a logistics certificate information extraction method that combines decoupled two-dimensional positional encoding, coordinate-supervised extraction, and multi-task instruction mixing. Before the downsampled two-dimensional visual features are projected into the language model embedding space, separate learnable positional embeddings are introduced for the row and column directions. The row and column position parameters are maintained independently and concatenated along the channel dimension to form a two-dimensional positional prior. This design supplements row-column relations, local adjacency, and layout structure with low parameter overhead, reduces the loss of spatial topology caused by image patch flattening, and improves the model’s ability to distinguish elongated text regions, dense fields, and adjacent areas. During information extraction, the conventional text-only question-answering task is reformulated as the joint generation of field text and region coordinates. The model outputs field content and normalized bounding boxes in a structured sequence, thereby establishing an explicit correspondence between field semantics and image regions and improving the spatial traceability and verifiability of the extraction results. To reduce the possible optimization conflict between text generation and coordinate prediction caused by direct coordinate supervision, three instruction tasks are further constructed, including joint extraction, visual localization, and text-only extraction. These tasks are randomly mixed at preset ratios, allowing the model to learn spatial localization while maintaining stable text recognition and instruction understanding. A logistics certificate dataset containing 4,000 images was constructed, covering driving licenses, qualification certificates, road transport permits, and vehicle licenses. The dataset includes multiple layouts and image acquisition conditions, with characteristics such as dense fields, elongated text regions, adjacent similar fields, and seal occlusion. Text content and corresponding bounding boxes were annotated for key fields and converted into instruction samples for text extraction, visual localization, and joint text-coordinate extraction. Public datasets, including DocVQA, XFUND-ZH, LSVT, and LLaVA-Instruct-ZH, were further used for mixed training to improve the model’s adaptability to general document structures, Chinese layouts, and complex visual interference. InternVL2.5-2B was used as the main backbone, while InternVL3-2B and InternVL3-8B were used for extended evaluation. Average Normalized Levenshtein Similarity (ANLS), Exact Match (EM), Mean Intersection over Union (Mean IoU), and Grounded Accuracy were adopted to evaluate text recognition, region localization, and text-coordinate consistency. The experimental results show that the proposed method clearly improves field localization while maintaining high text recognition performance. Compared with the InternVL2.5-2B baseline, the macro-average ANLS and Mean IoU over the four logistics certificate categories increase by 3.11 and 11.23 percentage points, respectively. On the road transport permit dataset, Mean IoU increases from 13.42% to 48.39%, while Grounded Accuracy increases from 17.32% to 62.09%. On the driving license dataset, ANLS and Grounded Accuracy reach 98.00% and 48.97%, respectively. Results from three independent runs on InternVL3-8B show macro-average improvements of 0.31, 1.00, 1.28, and 1.46 percentage points in ANLS, EM, Mean IoU, and Grounded Accuracy, respectively. The standard deviations remain close to those of the baseline, indicating stable gains at a larger model scale. Ablation experiments show that direct coordinate supervision alone makes it difficult to balance text generation and spatial localization. Adding multi-task instruction mixing or decoupled two-dimensional positional encoding clearly improves both Mean IoU and Grounded Accuracy, while their combination achieves the best overall performance. Evaluation on XFUND-ZH and DocVQA further shows that the proposed method improves spatial modeling for structured documents without weakening general document question-answering ability. These results confirm the effectiveness of jointly modeling explicit two-dimensional spatial priors and coordinate supervision for complex logistics certificate information extraction and provide technical support for traceable information extraction and structured document understanding in real-world applications.