> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Image OCR Extraction
> Print the text an image becomes before you ingest it: the vision-model transcription plus appended OCR text
A minimal guide to inspecting what Cognee extracts from an image. Run it against a screenshot, a scanned page, or a dense chart to see exactly which text would reach the knowledge graph — before spending an ingestion run on it.
## Before You Start
* Complete [Quickstart](/getting-started/quickstart) to understand basic operations
* Ensure you have [LLM Providers](/setup-configuration/llm-providers) configured with a vision-capable `LLM_MODEL` — the transcription step calls it
* Install the OCR engine: `pip install "cognee[rapidocr]"`
* Read [Loaders](/core-concepts/further-concepts/loaders) for how Cognee picks a loader per file and for the full set of image environment variables
* Have an image file on disk. The script reads `revenue_chart.png` from the example's [`multimedia_audio_image_processing_example_data/`](https://github.com/topoteretes/cognee/tree/dev/examples/guides/multimedia_audio_image_processing_example_data) directory in the Cognee repo — download it from there to reproduce the run exactly, or point `image_path` at your own file to check that one instead
## Code in Action
```python theme={null}
import asyncio
import os
import pathlib
from cognee.infrastructure.loaders.core.image_loader import ImageLoader
os.environ.setdefault("IMAGE_EXTRACTION_ENABLED", "true")
os.environ.setdefault("IMAGE_OCR_ENABLED", "true")
async def main():
image_path = os.path.join(
pathlib.Path(__file__).parent,
"multimedia_audio_image_processing_example_data/revenue_chart.png",
)
text = await ImageLoader().load(image_path, persist=False)
print("=== Text extracted from the image ===")
print(text)
if __name__ == "__main__":
asyncio.run(main())
```
## What Just Happened
### Step 1: Enable Extraction and OCR
```python theme={null}
import asyncio
import os
import pathlib
from cognee.infrastructure.loaders.core.image_loader import ImageLoader
os.environ.setdefault("IMAGE_EXTRACTION_ENABLED", "true")
os.environ.setdefault("IMAGE_OCR_ENABLED", "true")
```
`ImageLoader` is the loader `remember()` would pick for an image, imported directly here so you can call it on its own. `IMAGE_EXTRACTION_ENABLED` asks the vision model for entities, relationships, and verbatim text rather than a short caption; `IMAGE_OCR_ENABLED` adds a local OCR pass on top. Both are read when the image loads, and `setdefault` leaves any value you already set in the environment or `.env` untouched.
### Step 2: Point at an Image File
```python theme={null}
image_path = os.path.join(
pathlib.Path(__file__).parent,
"multimedia_audio_image_processing_example_data/revenue_chart.png",
)
```
The path is resolved relative to the script so the example runs from any working directory. Swap in a path to your own screenshot or scan to check what Cognee makes of it.
### Step 3: Load the Image and Print the Text
```python theme={null}
text = await ImageLoader().load(image_path, persist=False)
print("=== Text extracted from the image ===")
print(text)
```
`load()` runs the transcription and, when OCR is on, appends the recognized text to it. `persist=False` returns that text directly; the default `persist=True` writes it into Cognee's data directory and returns the file path instead — useful in a pipeline, unhelpful when you just want to read the result.
## Advanced Usage
The vision transcription comes first. If the OCR pass recognized anything, it follows under an `[OCR extracted text]` heading, so you can tell which half produced which text. OCR output is truncated at 8000 characters, and a failing OCR pass is logged and skipped rather than raising — so a result with no `[OCR extracted text]` block means OCR found nothing, was disabled, or failed.
To see what each stage contributes, run the script twice. `IMAGE_OCR_ENABLED="false"` gives the vision transcription alone; `IMAGE_EXTRACTION_ENABLED="false"` restores the legacy short-caption prompt. Both flags, and the transcription prompt and token-cap settings around them, are documented in [Loaders](/core-concepts/further-concepts/loaders).
Nothing here writes to memory. Once the extracted text looks right, pass the same image path to `remember()` — it selects `ImageLoader` for you and reads the same environment variables, so the text you just printed is the text that gets chunked and turned into graph memory.
See every format Cognee reads and each image transcription setting
Ingest the image once its extracted text looks right