The real estate sector remains highly dependent on manual document handling and verification, making processes inefficient and prone to fraud. This work presents a system that integrates optical character recognition (OCR), natural language processing (NLP), and verifiable credentials (VCs) to automate document extraction, verification, and management. The approach standardizes heterogeneous document formats into VCs and applies automated data matching to detect inconsistencies, while the blockchain provides a decentralized trust layer that reinforces transparency and integrity. A prototype was developed that comprises (i) an OCR-NLP extraction pipeline trained on synthetic datasets, (ii) a backend for credential issuance and management, and (iii) a frontend supporting issuer, holder, and verifier interactions. Experimental results show that the models achieve competitive accuracy across multiple document types and that the end-to-end pipeline reduces verification time while preserving reliability. The proposed framework demonstrates the potential to streamline real estate transactions, strengthen stakeholder trust, and enable scalable, secure digital processes.

翻译：房地产行业仍高度依赖人工文档处理与验证，导致流程效率低下且易受欺诈。本研究提出一种集成光学字符识别（OCR）、自然语言处理（NLP）与可验证凭证（VCs）的系统，以实现文档提取、验证与管理的自动化。该方法将异构文档格式标准化为可验证凭证，并应用自动化数据匹配以检测不一致性，同时区块链作为去中心化信任层增强了透明度与完整性。开发的原型系统包含：（i）基于合成数据集训练的OCR-NLP提取流水线，（ii）凭证签发与管理的后端服务，以及（iii）支持发行方、持有方与验证方交互的前端界面。实验结果表明，所提模型在多种文档类型上均达到具有竞争力的准确率，且端到端流水线在保持可靠性的同时显著缩短了验证时间。该框架展现出优化房地产交易流程、增强利益相关方信任及实现可扩展安全数字化流程的潜力。