quickstart.md

# Contributing

## Installation

Install [pixi](https://pixi.sh/latest/) for pulling conda/pip packages:

```bash
curl -fsSL https://pixi.sh/install.sh | sh
```

Create pixi environment and enter activated shell:

```bash
pixi s
```

Create a virtualenv and install nemo-retriever-ocr into it via `uv`:

```bash
uv venv \
&& uv pip install -e ./nemo-retriever-ocr -v
```

Assert that OCR inference libraries can now be imported successfully:

```bash
uv run python -c "import nemo_retriever_ocr; import nemo_retriever_ocr_cpp"
```

## Usage

`nemo_retriever_ocr.inference.pipeline.NemoRetrieverOCR` is the main entry point for performing OCR inference; it can be used to iterate over predictions for a given input image:

```python
from nemo_retriever_ocr.inference.pipeline import NemoRetrieverOCR

ocr = NemoRetrieverOCR()

predictions = ocr("ocr-example-input-1.png")

for pred in predictions:
    print(
        f"  - Text: '{pred['text']}', "
        f"Confidence: {pred['confidence']:.2f}, "
        f"Bbox: [left={pred['left']:.4f}, upper={pred['upper']:.4f}, right={pred['right']:.4f}, lower={pred['lower']:.4f}]"
    )
```

Or predictions can be superimposed on the input image for visualization:

```python
ocr(image_path, visualize=True)
```

The level of detection merging can be adjusted by modifying the `merge_level` argument (defaulting to "paragraph"):

```python
ocr(image_path, merge_level="word")      # leave detected words unmerged
ocr(image_path, merge_level="sentence")  # merge detected words into sentences
```

An example script `example.py` is provided for convenience:

```bash
uv run python example.py ocr-example-input-1.png
```

Detection merging can be adjusted by modifying the `--merge-level` option:

```bash
uv run python example.py ocr-example-input-1.png --merge-level word
uv run python example.py ocr-example-input-1.png --merge-level sentence
```