DeepSeek-OCR

Technical Specifications of deepseek-ocr

What is deepseek-ocr?

deepseek-ocr is an optical character recognition model designed to extract readable text from images and document-like visual inputs. It can process scanned pages, phone-captured photos, receipts, invoices, and interface screenshots, then return transcribed text in a form that preserves useful layout signals such as line breaks.

This makes deepseek-ocr useful for teams that need to convert unstructured visual content into machine-readable text. Typical workflows include digitizing archives, parsing business documents, indexing content for search, and feeding extracted text into automation or analytics pipelines.

Because the model focuses on image-to-text conversion, it is a practical choice when the goal is reliable transcription from visual sources rather than general image understanding. Its structured text output can also simplify downstream parsing, validation, and data extraction logic.

Main features of deepseek-ocr

• Image-to-text extraction: Converts text embedded in images and document captures into machine-readable output.
• Scanned document support: Works on scanned pages and digitized paperwork commonly used in enterprise workflows.
• Photographed content handling: Can process camera-captured pages, receipts, and forms where text appears in real-world conditions.
• Screenshot transcription: Extracts text from UI screenshots and application captures for indexing, testing, or automation.
• Layout-aware output: Preserves cues such as line breaks to make the transcription easier to read and parse.
• Document workflow friendly: Fits well into invoice intake, receipt processing, archival digitization, and back-office automation.
• Structured downstream usage: Produces text that can be passed into parsers, search systems, validation layers, or RPA pipelines.

How to access and integrate deepseek-ocr

Step 1: Sign Up for API Key

To get started, create an account on CometAPI and generate your API key from the dashboard. This key is required to authenticate all requests and route them through the CometAPI platform.

Step 2: Send Requests to deepseek-ocr API

Once you have your API key, send requests to the CometAPI endpoint using deepseek-ocr as the model ID. Include your input payload, authentication headers, and any application-specific parameters required by your OCR workflow.

curl https://api.cometapi.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_API_KEY" \
  -d '{
    "model": "deepseek-ocr",
    "input": [
      {
        "role": "user",
        "content": [
          {
            "type": "input_text",
            "text": "Extract the text from this document image."
          },
          {
            "type": "input_image",
            "image_url": "https://example.com/document.png"
          }
        ]
      }
    ]
  }'

Step 3: Retrieve and Verify Results

After the request completes, inspect the returned output text and verify that the extracted content matches the source image or document. For production OCR pipelines, it is a good practice to add confidence checks, post-processing rules, and human review for edge cases such as low-quality scans or complex layouts.