trocr-base-printed / README.md
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---
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}]
```