标题： 基于OCR和紧凑视觉-语言模型的金融文档多阶段字段提取 显示英文标题

标题： Multi-Stage Field Extraction of Financial Documents with OCR and Compact Vision-Language Models

摘要： 财务文件是监管机构、审计师和金融机构的重要信息来源，特别是用于评估中小企业的财富和合规性。 然而，中小企业文件通常难以解析。 它们很少以数字形式生成，而是作为扫描图像分发，这些图像无法被机器读取。 扫描本身分辨率低，受倾斜或旋转影响，并且通常包含嘈杂的背景。 这些文件还往往具有异质性，在同一份报告中混合了叙述、表格、图表和多语言内容。 这些特点对自动化信息提取构成了重大挑战，尤其是在依赖端到端大型视觉语言模型时，这些模型计算成本高，对噪声敏感，并且在处理数百页的文件时速度较慢。 我们提出了一种多阶段的流程，结合传统图像处理模型和OCR提取，以及紧凑型视觉语言模型，用于大规模财务文件的结构化字段提取。 我们的方法首先进行图像预处理，包括分割、方向检测和尺寸归一化。 然后应用多语言OCR来恢复页面级文本。 在分析文本信息后，检索出连贯的页面部分。 最后，在这些缩小的范围内操作紧凑型视觉语言模型，以提取结构化的财务指标。 我们的方法使用了一个内部的多语言、扫描财务文档语料库进行评估。 结果表明，紧凑型视觉语言模型结合多阶段流程，相对于直接将整个文档输入大型视觉语言模型，字段级别的准确率提高了8.8倍，仅消耗0.7%的GPU成本和92.6%更少的端到端服务延迟。 显示英文摘要

摘要： Financial documents are essential sources of information for regulators, auditors, and financial institutions, particularly for assessing the wealth and compliance of Small and Medium-sized Businesses. However, SMB documents are often difficult to parse. They are rarely born digital and instead are distributed as scanned images that are none machine readable. The scans themselves are low in resolution, affected by skew or rotation, and often contain noisy backgrounds. These documents also tend to be heterogeneous, mixing narratives, tables, figures, and multilingual content within the same report. Such characteristics pose major challenges for automated information extraction, especially when relying on end to end large Vision Language Models, which are computationally expensive, sensitive to noise, and slow when applied to files with hundreds of pages. We propose a multistage pipeline that leverages traditional image processing models and OCR extraction, together with compact VLMs for structured field extraction of large-scale financial documents. Our approach begins with image pre-processing, including segmentation, orientation detection, and size normalization. Multilingual OCR is then applied to recover page-level text. Upon analyzing the text information, pages are retrieved for coherent sections. Finally, compact VLMs are operated within these narrowed-down scopes to extract structured financial indicators. Our approach is evaluated using an internal corpus of multi-lingual, scanned financial documents. The results demonstrate that compact VLMs, together with a multistage pipeline, achieves 8.8 times higher field level accuracy relative to directly feeding the whole document into large VLMs, only at 0.7 percent of the GPU cost and 92.6 percent less end-to-end service latency.