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* docs(cli): document device-code login and config reset guidance; renumber sections * docs(cli): fix duplicate numbering (renumber Login/API Keys/Configuration sections) * docs: Fix webhook documentation to include meta dict in all webhook payloads - Add note explaining that meta objects from kickoff requests are included in all webhook payloads - Update webhook examples to show proper payload structure including meta field - Fix webhook examples to match actual API implementation - Apply changes to English, Korean, and Portuguese documentation Resolves the documentation gap where meta dict passing to webhooks was not documented despite being implemented in the API. * WIP: CrewAI docs theme, changelog, GEO, localization * docs(cli): fix merge markers; ensure mode: "wide"; convert ASCII tables to Markdown (en/pt-BR/ko) * docs: add group icons across locales; split Automation/Integrations; update tools overviews and links
91 lines
1.8 KiB
Plaintext
91 lines
1.8 KiB
Plaintext
---
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title: OCR Tool
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description: The `OCRTool` extracts text from local images or image URLs using an LLM with vision.
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icon: image
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mode: "wide"
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---
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# `OCRTool`
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## Description
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Extract text from images (local path or URL). Uses a vision‑capable LLM via CrewAI’s LLM interface.
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## Installation
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No extra install beyond `crewai-tools`. Ensure your selected LLM supports vision.
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## Parameters
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### Run Parameters
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- `image_path_url` (str, required): Local image path or HTTP(S) URL.
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## Examples
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### Direct usage
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```python Code
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from crewai_tools import OCRTool
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print(OCRTool().run(image_path_url="/tmp/receipt.png"))
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```
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### With an agent
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```python Code
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from crewai import Agent, Task, Crew
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from crewai_tools import OCRTool
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ocr = OCRTool()
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agent = Agent(
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role="OCR",
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goal="Extract text",
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tools=[ocr],
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)
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task = Task(
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description="Extract text from https://example.com/invoice.jpg",
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expected_output="All detected text in plain text",
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agent=agent,
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)
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crew = Crew(agents=[agent], tasks=[task])
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result = crew.kickoff()
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```
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## Notes
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- Ensure the selected LLM supports image inputs.
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- For large images, consider downscaling to reduce token usage.
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- You can pass a specific LLM instance to the tool (e.g., `LLM(model="gpt-4o")`) if needed, matching the README guidance.
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## Example
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```python Code
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from crewai import Agent, Task, Crew
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from crewai_tools import OCRTool
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tool = OCRTool()
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agent = Agent(
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role="OCR Specialist",
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goal="Extract text from images",
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backstory="Vision‑enabled analyst",
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tools=[tool],
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verbose=True,
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)
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task = Task(
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description="Extract text from https://example.com/receipt.png",
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expected_output="All detected text in plain text",
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agent=agent,
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)
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crew = Crew(agents=[agent], tasks=[task])
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result = crew.kickoff()
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```
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