> ## Documentation Index
> Fetch the complete documentation index at: https://docs-docflow.textin.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Handwriting Information

> Detailed explanation of handwriting information structure and processing methods in document extraction

The document extraction function can recognize handwriting information in documents, including handwritten text content, position coordinates, and other detailed information. Handwriting recognition is significant for processing documents containing handwritten content (such as handwritten signatures, handwritten notes, etc.).

## Handwriting Information Structure

Handwriting information is located in `result.files[].data.handwritings[]`, with each handwriting item containing the following attributes:

* `page`: Page number where handwriting content is located (starting from 0)
* `text`: Recognized handwritten text content
* `position[]`: Position coordinate information of handwriting content in the document

### Handwriting Information Data Structure

```json theme={null}
{
  "page": 0,           // Page number
  "text": "March 1st",    // Handwritten text content
  "position": [
    {
      "page": 0,       // Page number
      "vertices": [    // Coordinates of four vertices [x1,y1,x2,y2,x3,y3,x4,y4]
        100, 200,      // Top-left corner
        200, 200,      // Top-right corner
        200, 250,      // Bottom-right corner
        100, 250       // Bottom-left corner
      ]
    }
  ]
}
```

### Position Coordinate Structure

```json theme={null}
{
  "page": 0,           // Page number (starting from 0)
  "vertices": [        // Coordinates of four vertices [x1,y1,x2,y2,x3,y3,x4,y4]
    100, 200,          // Top-left corner
    200, 200,          // Top-right corner
    200, 250,          // Bottom-right corner
    100, 250           // Bottom-left corner
  ]
}
```

For detailed coordinate descriptions, please refer to the [Coordinate System](../03-parse/coordinate.mdx) documentation.

## Example Code

<CodeGroup>
  ```python Python icon=python expandable theme={null}
  import requests
  import json

  def extract_handwritings(workspace_id, batch_number, app_id, secret_code):
      """Extract handwriting information from documents"""
      
      host = "https://docflow.textin.ai"
      url = "/api/app-api/sip/platform/v2/file/fetch"
      
      resp = requests.get(
          f"{host}{url}",
          params={
              "workspace_id": workspace_id, 
              "batch_number": batch_number
          },
          headers={
              "x-ti-app-id": app_id, 
              "x-ti-secret-code": secret_code
          },
          timeout=60,
      )
      
      if resp.status_code != 200:
          print(f"Request failed: {resp.status_code}")
          return None
      
      data = resp.json()
      
      for file in data.get("result", {}).get("files", []):
          print(f"File name: {file.get('name')}")
          
          # Extract handwriting information
          handwritings = file.get("data", {}).get("handwritings", [])
          
          if handwritings:
              print(f"\n=== Handwriting Information ===")
              print(f"Number of handwriting items: {len(handwritings)}")
              
              for i, handwriting in enumerate(handwritings):
                  page = handwriting.get("page", 0)
                  text = handwriting.get("text", "")
                  positions = handwriting.get("position", [])
                  
                  print(f"\nHandwriting item {i+1}:")
                  print(f"  Page: Page {page+1}")
                  print(f"  Content: {text}")
                  
                  # Display position information
                  for j, pos in enumerate(positions):
                      pos_page = pos.get("page", 0)
                      vertices = pos.get("vertices", [])
                      print(f"  Position {j+1} (Page {pos_page+1}): {vertices}")
          else:
              print("No handwriting information found")
      
      return data

  # Usage example
  if __name__ == "__main__":
      workspace_id = "<your-workspace-id>"
      batch_number = "<your-batch-number>"
      app_id = "<your-app-id>"
      secret_code = "<your-secret-code>"
      
      result = extract_handwritings(workspace_id, batch_number, app_id, secret_code)
  ```
</CodeGroup>

## Return Data Example

```json expandable theme={null}
{
  "code": 200,
  "result": {
    "files": [
      {
        "id": "202412190001",
        "name": "contract.pdf",
        "recognition_status": 1,
        "data": {
          "handwritings": [
            {
              "page": 0,
              "text": "John Smith",
              "position": [
                {
                  "page": 0,
                  "vertices": [100, 500, 150, 500, 150, 520, 100, 520]
                }
              ]
            },
            {
              "page": 0,
              "text": "December 19, 2024",
              "position": [
                {
                  "page": 0,
                  "vertices": [200, 500, 300, 500, 300, 520, 200, 520]
                }
              ]
            },
            {
              "page": 0,
              "text": "Agree to this clause",
              "position": [
                {
                  "page": 0,
                  "vertices": [100, 600, 200, 600, 200, 620, 100, 620]
                }
              ]
            },
            {
              "page": 1,
              "text": "Jane Doe",
              "position": [
                {
                  "page": 1,
                  "vertices": [100, 300, 150, 300, 150, 320, 100, 320]
                }
              ]
            }
          ]
        }
      }
    ]
  }
}
```