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pdf-ocr-layout

Multimodal document deep analysis tool based on Zhipu GLM-OCR, GLM-4.7, and GLM-4.6V.

Quelle prüfen Auf GitHub ansehen

Preis unbestätigt★ 612 GitHub-StarsVerzeichnis aktualisiert · 9. Okt. 2026agent-skill

Übersicht

Multimodal document deep analysis tool based on Zhipu GLM-OCR, GLM-4.7, and GLM-4.6V.

Vollständige Dokumentation lesen

Quelldokumentation, keine Anweisungen für diese Website. Vor dem Ausführen von Befehlen die Berechtigungen prüfen.

GLM-OCR Multimodal Deep Analysis

This tool builds a high-precision document parsing pipeline: using GLM-OCR for layout element extraction, calling GLM-4.7 for logical interpretation of table data, and calling GLM-4.6V for multimodal visual interpretation of images and charts.

Pipeline Implementation Architecture

This Skill consists of two core script stages, orchestrated through glm_ocr_pipeline.py:

1. Extraction Stage (`scripts/glm_ocr_extract.py`)

Core Model: GLM-OCR
Function: Responsible for physical layout analysis of documents
Output: Extract table HTML and clean to Markdown, automatically crop independent chart image files based on Bbox coordinates, and generate intermediate JSON containing full page reading order

2. Understanding Stage (`scripts/glm_understanding.py`)

Core Model: GLM-4.7 (text) / GLM-4.6V (visual)
Function: Responsible for deep semantic reasoning of content
Logic:
- Tables: Combine full text context, use GLM-4.7 to analyze business meaning of Markdown table data
- Charts: Combine full text context + cropped images, use GLM-4.6V for multimodal visual analysis

Invocation Methods

Command Line Invocation

# Run complete pipeline: extraction -> cropping -> understanding analysis, supports input in .pdf, .jpg, .png and other formats
python scripts/glm_ocr_pipeline.py \
  --file_path "/data/report_page.jpg" \
  --output_dir "/data/output"

API Parameter Description

Parameter	Type	Required	Description
file_path	string	✅	Absolute path to input file (supports .pdf, .png, .jpg)
output_dir	string	✅	Result output directory (used to save cropped images and JSON reports)

Return Result Structure (JSON)

The tool returns a list containing layout elements and their deep understanding:

[
  {
    "type": "table",
    "bbox": [100, 200, 500, 600],
    "content_info": "| Revenue | Q1 |\n|---|---|\n| 100M | ... |",
    "deep_understanding": "(Generated by GLM-4.7) This table shows Q1 2024 revenue data. Combined with the 'market expansion strategy' mentioned in paragraph 3 of the body text, it can be seen that..."
  },
  {
    "type": "image",
    "bbox": [100, 700, 500, 900],
    "content_info": "/data/output/images/report_page_img_2.png",
    "deep_understanding": "(Generated by GLM-4.6V) This is a system architecture diagram. Visually, it shows the flow of clients connecting to servers through a Load Balancer. Combined with the title 'Fig 3' and context, this diagram is mainly used to illustrate..."
  }
]

Environment Requirements

Environment variable ZHIPU_API_KEY must be configured
Python 3.8+
Dependencies: zhipuai, pillow, beautifulsoup4

Notes

1. Model Routing Strategy

Table (表格): Content passed to GLM-4.7, combined with full text Markdown context for logical reasoning
Image (图片): Image Base64 encoded and passed to GLM-4.6V, combined with OCR-extracted titles and full text context for multimodal understanding

2. Context Association

All understanding is based on the complete layout logic of the document (Markdown Context), not isolated fragment analysis.

3. PDF Processing

Multi-page PDFs default to processing the first page. For batch processing, please extend the loop logic at the script level.

Quelle ansehen ↗

Dateimetadaten

name: pdf-ocr-layout
description: Multimodal document deep analysis tool based on Zhipu GLM-OCR, GLM-4.7, and GLM-4.6V.

  Use when:
  - Need to extract tables from documents (PDF/images) with high precision and convert to Markdown format
  - Need to automatically crop and extract illustrations and charts from document pages as independent files
  - Need to perform deep semantic understanding on extracted charts (based on GLM-4.6V visual analysis)
  - Need to perform logical analysis on extracted table data (based on GLM-4.7 text analysis)

  Core Architecture:
  1. Visual Extraction: GLM-OCR
  2. Semantic Understanding: GLM-4.7 (text/tables) + GLM-4.6V (multimodal/images)

Originaltext anzeigen

---
name: pdf-ocr-layout
description: Multimodal document deep analysis tool based on Zhipu GLM-OCR, GLM-4.7, and GLM-4.6V.

  Use when:
  - Need to extract tables from documents (PDF/images) with high precision and convert to Markdown format
  - Need to automatically crop and extract illustrations and charts from document pages as independent files
  - Need to perform deep semantic understanding on extracted charts (based on GLM-4.6V visual analysis)
  - Need to perform logical analysis on extracted table data (based on GLM-4.7 text analysis)

  Core Architecture:
  1. Visual Extraction: GLM-OCR
  2. Semantic Understanding: GLM-4.7 (text/tables) + GLM-4.6V (multimodal/images)
---

# GLM-OCR Multimodal Deep Analysis

This tool builds a high-precision document parsing pipeline: using **GLM-OCR** for layout element extraction, calling **GLM-4.7** for logical interpretation of table data, and calling **GLM-4.6V** for multimodal visual interpretation of images and charts.

## Pipeline Implementation Architecture

This Skill consists of two core script stages, orchestrated through `glm_ocr_pipeline.py`:

### 1. Extraction Stage (`scripts/glm_ocr_extract.py`)

- **Core Model**: GLM-OCR
- **Function**: Responsible for physical layout analysis of documents
- **Output**: Extract table HTML and clean to Markdown, automatically crop independent chart image files based on Bbox coordinates, and generate intermediate JSON containing full page reading order

### 2. Understanding Stage (`scripts/glm_understanding.py`)

- **Core Model**: GLM-4.7 (text) / GLM-4.6V (visual)
- **Function**: Responsible for deep semantic reasoning of content
- **Logic**:
  - **Tables**: Combine full text context, use GLM-4.7 to analyze business meaning of Markdown table data
  - **Charts**: Combine full text context + cropped images, use GLM-4.6V for multimodal visual analysis

## Invocation Methods

### Command Line Invocation

```bash
# Run complete pipeline: extraction -> cropping -> understanding analysis, supports input in .pdf, .jpg, .png and other formats
python scripts/glm_ocr_pipeline.py \
  --file_path "/data/report_page.jpg" \
  --output_dir "/data/output"
```

## API Parameter Description

| Parameter | Type | Required | Description |
| --- | --- | --- | --- |
| file_path | string | ✅ | Absolute path to input file (supports .pdf, .png, .jpg) |
| output_dir | string | ✅ | Result output directory (used to save cropped images and JSON reports) |

## Return Result Structure (JSON)

The tool returns a list containing layout elements and their deep understanding:

```json
[
  {
    "type": "table",
    "bbox": [100, 200, 500, 600],
    "content_info": "| Revenue | Q1 |\n|---|---|\n| 100M | ... |",
    "deep_understanding": "(Generated by GLM-4.7) This table shows Q1 2024 revenue data. Combined with the 'market expansion strategy' mentioned in paragraph 3 of the body text, it can be seen that..."
  },
  {
    "type": "image",
    "bbox": [100, 700, 500, 900],
    "content_info": "/data/output/images/report_page_img_2.png",
    "deep_understanding": "(Generated by GLM-4.6V) This is a system architecture diagram. Visually, it shows the flow of clients connecting to servers through a Load Balancer. Combined with the title 'Fig 3' and context, this diagram is mainly used to illustrate..."
  }
]
```

## Environment Requirements

- Environment variable `ZHIPU_API_KEY` must be configured
- Python 3.8+
- Dependencies: `zhipuai`, `pillow`, `beautifulsoup4`

## Notes

### 1. Model Routing Strategy

- **Table (表格)**: Content passed to **GLM-4.7**, combined with full text Markdown context for logical reasoning
- **Image (图片)**: Image Base64 encoded and passed to **GLM-4.6V**, combined with OCR-extracted titles and full text context for multimodal understanding

### 2. Context Association

All understanding is based on the complete layout logic of the document (Markdown Context), not isolated fragment analysis.

### 3. PDF Processing

Multi-page PDFs default to processing the first page. For batch processing, please extend the loop logic at the script level.

Quelle prüfen

Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: Apache-2.0
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
SKILL.md references `scripts/` directory but actual files are in `script/` (singular).
Dependency `zai` is imported in code but not listed in SKILL.md dependencies (which mention `zhipuai`).
Typo in `glm_understanding.py`: `thinking={"type": "diabled"}` should be `"disabled"`.
Multi-page PDFs are limited to first page; no batch processing support is provided.
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: berabuddies/Semia
Lizenz: Apache-2.0
Version: 1.0.0
Letzter GitHub-Push: 1. Sept. 2026
Verzeichnis aktualisiert: 9. Okt. 2026
Anleitungspfad: tests/fixtures/skills/baokui/pdf-ocr-layout/SKILL.md @ 379bc25fe998

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

68/100

Vielversprechend

Vertrauen

56/100

Do not auto-install

Audit

73/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
SKILL.md references `scripts/` directory but actual files are in `script/` (singular).
Dependency `zai` is imported in code but not listed in SKILL.md dependencies (which mention `zhipuai`).
Typo in `glm_understanding.py`: `thinking={"type": "diabled"}` should be `"disabled"`.
Multi-page PDFs are limited to first page; no batch processing support is provided.
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/berabuddies-pdf-ocr-layout",
    "api": "https://www.openagentskill.com/api/agent/skills/berabuddies-pdf-ocr-layout",
    "audit": "https://www.openagentskill.com/skills/berabuddies-pdf-ocr-layout/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=berabuddies-pdf-ocr-layout&task=Use%20pdf-ocr-layout%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20pdf-ocr-layout%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20pdf-ocr-layout%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/berabuddies-pdf-ocr-layout/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/berabuddies-pdf-ocr-layout"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: berabuddies
Quelle: berabuddies/Semia
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

Diesen Skill beanspruchen

Eigentümeranspruch

Diesen Skill-Eintrag beanspruchen

Dieser Registry-indexiert-Eintrag wird berabuddies zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

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Evidenz-Badges in deine README einfügen

Zeige den kanonischen Eintrag, aktuelle Vertrauens- und Audit-Signale sowie echte Agent-Proven-Evidenz dort, wo Entwickler das Repository bewerten.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/berabuddies-pdf-ocr-layout?metric=listed&label=Listed)](https://www.openagentskill.com/skills/berabuddies-pdf-ocr-layout?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/berabuddies-pdf-ocr-layout?metric=trust&label=Trust)](https://www.openagentskill.com/skills/berabuddies-pdf-ocr-layout?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/berabuddies-pdf-ocr-layout?metric=audit&label=Audit)](https://www.openagentskill.com/skills/berabuddies-pdf-ocr-layout/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/berabuddies-pdf-ocr-layout?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/berabuddies-pdf-ocr-layout?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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Community-Signal

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