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pdf-ocr-layout

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

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Harga belum dikonfirmasi★ 612 Star GitHubDirektori diperbarui · 9 Okt 2026agent-skill

Ringkasan

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

Baca dokumentasi lengkap

Dokumentasi sumber, bukan instruksi untuk situs ini. Periksa izin sebelum menjalankan perintah.

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.

Lihat sumber ↗

Metadata berkas

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)

Lihat teks asli

---
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.

Tinjau sumber

Harga dan biaya penggunaan

Dapatkan skill: Harga belum dikonfirmasi
Jalankan: Persyaratan belum dikonfirmasi. Periksa biaya agen, API, dan layanan di sumbernya.
Lisensi: Apache-2.0
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Lisensi: 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

Buka audit lengkap

Daftar alat adalah petunjuk metadata, bukan kompatibilitas teruji. Prompt adalah saran.

Mulai dengan tugas kecil

1Baca sumber dan pastikan masukan, keluaran, dependensi, serta izin.
2Minta rencana dari agent. Setujui pengaturan dan biaya sebelum uji terisolasi.
3Periksa hasil dan berkas yang berubah. Laporkan hanya yang dijalankan dan simpan revisi sumber.

Periksa dependensi, kunci API, dan biaya layanan pihak ketiga pada sumber. Repositori publik tidak berarti semua layanan gratis.

Sumber dan catatan penggunaan

Terindeks

Metadata dan tinjauan bersifat saran. Popularitas, penemuan sumber, dan keberhasilan eksekusi adalah fakta berbeda.

Repositori sumber: berabuddies/Semia
Lisensi: Apache-2.0
Versi: 1.0.0
Push GitHub terakhir: 1 Sep 2026
Direktori diperbarui: 9 Okt 2026
Jalur instruksi: tests/fixtures/skills/baokui/pdf-ocr-layout/SKILL.md @ 379bc25fe998

Versi dilaporkan dalam metadata direktori; periksa rilis sumber.

Kualitas

68/100

Menjanjikan

Kepercayaan

56/100

Do not auto-install

Audit

73/100

Perlu ditinjau

Buka audit lengkap Lihat laporan evaluasi

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: —
Hasil: —

Menyalin bukan memasang. Jumlah instalasi memerlukan laporan berhasil dan bukan jaminan kualitas menyeluruh.

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Detail lainnya

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      "repository": "https://github.com/berabuddies/Semia/tree/main/tests/fixtures/skills/baokui/pdf-ocr-layout",
      "install": "npx skills add berabuddies/Semia --skill pdf-ocr-layout",
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      "documentation": "Usable metadata, review docs",
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      "Multi-page PDFs are limited to first page; no batch processing support is provided.",
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      "Safety: 33/100 Avoid automatic install",
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      "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"
  }
}

Untuk kreator

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator: berabuddies
Sumber: berabuddies/Semia
Diindeks oleh: Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan berabuddies, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit berbagi

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![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)

Pratinjau badge Buka audit Kit kreator

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.