pdf-ocr-layout: Design & UI for AI Agents | OpenAgentSkill

Overview

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

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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.

View source ↗

File metadata

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)

View original text

---
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.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

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).

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Source & trust

Indexed

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License: Apache-2.0
Version: 1.0.0
Last GitHub push: Sep 1, 2026
Registry updated: Sep 5, 2026
Instruction path: tests/fixtures/skills/baokui/pdf-ocr-layout/SKILL.md @ 379bc25fe998

Version reported in registry metadata; check source releases before relying on it.

Quality

74/100

Strong

Trust

57/100

Do not auto-install

Audit

76/100

Needs review

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More details

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Creator: berabuddies
Source: berabuddies/Semia
Indexed by: OpenAgentSkill community index

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