---
tags:
- trocr
- image-to-text
- endpoints-template
library_name: generic
---

# Fork of [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) for an `OCR` Inference endpoint.

This repository implements a `custom` task for `ocr-detection` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/trocr-base-printed/blob/main/pipeline.py).

To use deploy this model as an Inference Endpoint, you have to select `Custom` as the task to use the `pipeline.py` file. -> _double check if it is selected_

## Run Request 

The endpoint expects the image to be served as `binary`. Below is an curl and python example

#### cURL 

1. get image 

```bash
wget https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg -O test.jpg
```

2. send cURL request

```bash
curl --request POST \
  --url https://{ENDPOINT}/ \
  --header 'Content-Type: image/jpg' \
  --header 'Authorization: Bearer {HF_TOKEN}' \
  --data-binary '@test.jpg'
```

3. the expected output

```json
{"text": "INDLUS THE"
```

#### Python 


1. get image 

```bash
wget https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg -O test.jpg
```

2. run request

```python
import json
from typing import List
import requests as r
import base64

ENDPOINT_URL = ""
HF_TOKEN = ""


def predict(path_to_image: str = None, candiates: List[str] = None):
    with open(path_to_image, "rb") as i:
        b64 = base64.b64encode(i.read())

    payload = {"inputs": {"image": b64.decode("utf-8"), "candiates": candiates}}
    response = r.post(
        ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload
    )
    return response.json()


prediction = predict(
    path_to_image="palace.jpg", candiates=["sea", "palace", "car", "ship"]
)
```

expected output

```python
[{'label': 'palace', 'score': 0.9996134638786316},
 {'label': 'car', 'score': 0.0002602009626571089},
 {'label': 'ship', 'score': 0.00011758189066313207},
 {'label': 'sea', 'score': 8.666840585647151e-06}]
```