Hybrid Multi-Phase Page Matching and Multi-Layer Diff Detection for Japanese Building Permit Document Review

• URL: http://arxiv.org/abs/2604.19770v1
• Date: Fri, 27 Mar 2026 09:01:30 GMT
• Title: Hybrid Multi-Phase Page Matching and Multi-Layer Diff Detection for Japanese Building Permit Document Review
• Abstract summary: Building permit review in Japan requires cross-referencing large PDF document sets across revision cycles.<n>The algorithm combines longest common subsequence (LCS) structural alignment, a seven-phase consensus matching pipeline, and a dynamic programming optimal alignment stage.<n> Evaluation on real-world permit document sets achieves F1=0.80 and precision=1.00 on a manually annotated ground-truth benchmark.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a hybrid multi-phase page matching algorithm for automated comparison of Japanese building permit document sets. Building permit review in Japan requires cross-referencing large PDF document sets across revision cycles, a process that is labor-intensive and error-prone when performed manually. The algorithm combines longest common subsequence (LCS) structural alignment, a seven-phase consensus matching pipeline, and a dynamic programming optimal alignment stage to robustly pair pages across revisions even when page order, numbering, or content changes substantially. A subsequent multi-layer diff engine -- comprising text-level, table-level, and pixel-level visual differencing -- produces highlighted difference reports. Evaluation on real-world permit document sets achieves F1=0.80 and precision=1.00 on a manually annotated ground-truth benchmark, with zero false-positive matched pairs.

Related papers

• HPD-Parsing: Hierarchical Parallel Document Parsing [14.216874211667358]
  We introduce HPD-Parsing, which replaces full-page autoregressive generation with a Hierarchical Parallel Decoding paradigm.<n> Experiments on public benchmarks show that HPD-Parsing achieves 4,752 tokens per second, delivering $2.62times$ the throughput of the fastest existing document parsing model.<n>These results establish hierarchical parallel decoding as an effective alternative to full-page autoregressive generation, opening a new direction for efficient unified document parsing.
  arXiv  Detail & Related papers  (2026-07-21T08:25:32Z)
• RT-DocLayout: Real-Time End-to-End Document Layout Analysis with Reading Order in the Wild [14.715243408844058]
  We present RT-Doc, a highly efficient end-to-end framework for document layout analysis.<n>The proposed model unifies classification, pixel-level segmentation, and geometric order reading order prediction for layout elements.<n> RT-Doc significantly improves full-document reconstruction quality, providing a scalable and practical foundation for real-world document intelligence systems.
  arXiv  Detail & Related papers  (2026-06-22T13:48:39Z)
• Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training [23.87978106727431]
  We propose a data-training co-design framework for robust end-to-end document parsing.<n>Our method achieves superior accuracy and robustness across both scanned/digital and real-world captured scenarios.
  arXiv  Detail & Related papers  (2026-03-25T03:19:09Z)
• Training-Free Acceleration for Document Parsing Vision-Language Model with Hierarchical Speculative Decoding [102.88996030431662]
  We propose a training-free and highly efficient acceleration method for document parsing tasks.<n>Inspired by speculative decoding, we employ a lightweight document parsing pipeline as a draft model to predict batches of future tokens.<n>We demonstrate the effectiveness of our approach on the general-purpose OmniDocBench.
  arXiv  Detail & Related papers  (2026-02-13T14:22:10Z)
• MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patterns [80.05126590825121]
  MonkeyOCR v1.5 is a unified vision-language framework that enhances both layout understanding and content recognition.<n>To address complex table structures, we propose a visual consistency-based reinforcement learning scheme.<n>Two specialized modules, Image-Decoupled Table Parsing and Type-Guided Table Merging, are introduced to enable reliable parsing of tables.
  arXiv  Detail & Related papers  (2025-11-13T15:12:17Z)
• MMESGBench: Pioneering Multimodal Understanding and Complex Reasoning Benchmark for ESG Tasks [56.350173737493215]
  Environmental, Social, and Governance (ESG) reports are essential for evaluating sustainability practices, ensuring regulatory compliance, and promoting financial transparency.<n>MMESGBench is a first-of-its-kind benchmark dataset to evaluate multimodal understanding and complex reasoning across structurally diverse and multi-source ESG documents.<n>MMESGBench comprises 933 validated QA pairs derived from 45 ESG documents, spanning across seven distinct document types and three major ESG source categories.
  arXiv  Detail & Related papers  (2025-07-25T03:58:07Z)
• Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding [0.0]
  Retrieval-Augmented Generation (RAG) systems have revolutionized information retrieval and question answering.<n>Traditional text-based chunking methods struggle with complex document structures, multi-page tables, embedded figures, and contextual dependencies across page boundaries.<n>We present a novel multimodal document chunking approach that leverages Large Multimodal Models (LMMs) to process PDF documents in batches.
  arXiv  Detail & Related papers  (2025-06-19T05:11:43Z)
• Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing [46.14775667559124]
  layoutRL is an end-to-end reinforcement learning framework that trains models to be explicitly layout-aware.<n>We will publicly release our code and dataset to accelerate progress in robust document understanding.
  arXiv  Detail & Related papers  (2025-06-01T15:19:52Z)
• The Power of Summary-Source Alignments [62.76959473193149]
  Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection. alignment of corresponding sentences between a reference summary and its source documents has been leveraged to generate training data. This paper proposes extending the summary-source alignment framework by applying it at the more fine-grained proposition span level.
  arXiv  Detail & Related papers  (2024-06-02T19:35:19Z)
• End-to-End Page-Level Assessment of Handwritten Text Recognition [69.55992406968495]
  HTR systems increasingly face the end-to-end page-level transcription of a document. Standard metrics do not take into account the inconsistencies that might appear. We propose a two-fold evaluation, where the transcription accuracy and the RO goodness are considered separately.
  arXiv  Detail & Related papers  (2023-01-14T15:43:07Z)
• Multilevel Text Alignment with Cross-Document Attention [59.76351805607481]
  Existing alignment methods operate at a single, predefined level. We propose a new learning approach that equips previously established hierarchical attention encoders for representing documents with a cross-document attention component.
  arXiv  Detail & Related papers  (2020-10-03T02:52:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.