# paddle_ocr_service

**Repository Path**: flying_red_pig/paddle_ocr_service

## Basic Information

- **Project Name**: paddle_ocr_service
- **Description**: 为 paddle ocr 提供一个以 docker 为基础的 web 服务
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-25
- **Last Updated**: 2025-05-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# UI5 OCR Service API

The OCR API allows you to extract text from images using Optical Character Recognition (OCR) technology. Send a POST request with an image file and receive the extracted text in the response.

## Endpoint

```
POST http://{HOST_URL}:8080/ocr
```

## Request

### Headers

- `Content-Type`: multipart/form-data; boundary=your_boundary_here

### Body

- `file`: The image file you want to extract text from. Supported formats include JPEG, PNG, and GIF.
- `data`: A JSON object containing additional information for the OCR process. Currently, only the language parameter is supported.

Example:

```json
{
  "lang": "en"
}
```
#### data parameters
1. `lang` (optional): The language used for OCR processing. Default is `'en'` (English). You can pass any language supported by the `PaddleOCR` class. The parameters detail please check `https://github.com/Mushroomcat9998/PaddleOCR/blob/main/doc/doc_en/multi_languages_en.md#language_abbreviations`.



## Response

The API will respond with a JSON object containing the extracted text.

### Success

If the OCR process is successful, the response will have a `message` field containing the extracted text.

Example:

```json
{
  "message": [
    "Extracted text from the image"
  ]
}
```

### Failure

- Status code: `400`
- Content-Type: `application/json`
- Body: A JSON object containing the error message.


## Example Usage

Here is an example of using the OCR API with JavaScript and Axios:

```javascript
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');

async function uploadImage(imagePath) {
  const formData = new FormData();
  const data = { lang: 'en' };
  formData.append('file', fs.createReadStream(imagePath));
  formData.append('data', JSON.stringify(data));
  try {
    const response = await axios.post('http://localhost:8080/ocr', formData, {
      headers: {
        'Content-Type': `multipart/form-data; boundary=${formData.getBoundary()}`,
      },
    });
    console.log('Upload success:', response.data.message[0]);
  } catch (error) {
    console.error('Upload failed:', error.response);
  }
}

const imagePath = './img2.png';
uploadImage(imagePath);
```

## Deploy

```shell
docker-compose up
```