The command-line tool provides quick access to the library's features directly from your terminal.

lens_scan <image_source> [ocr_lang] [options]

• <image_source>: Path to a local image file or an image URL.
• [ocr_lang] (optional): BCP 47 language code for OCR (e.g., 'en', 'ja'). If omitted, the API will attempt to auto-detect the language.

Options

Examples

1. Basic OCR and Translation

Auto-detects the source language on the image and translates it to English. This is the most common use case.

lens_scan "path/to/your/image.png" -t en

2. Get Segmented Text Blocks (for Comics/Manga)

Ideal for images with multiple, separate text boxes. This command outputs each recognized text block individually, making it perfect for translating comics or complex documents.

lens_scan "path/to/manga.jpg" ja -b

• -b is the alias for --output-blocks.

3. Get Individual Text Lines

Outputs each recognized line of text along with its geometry.

lens_scan "path/to/document.png" --output-lines

• -ol is the alias for --output-lines.

4. Get Coordinates of All Individual Words

Outputs a detailed JSON array containing every single recognized word and its precise geometric data (center, size, angle). Useful for programmatic analysis or custom overlays.

lens_scan "path/to/diagram.png" --get-coords

5. Translate, Save Overlay, and Copy to Clipboard

A power-user workflow. This command will:

1. OCR a Japanese image.
2. Translate it to Russian.
3. Save a new image named translated_manga.png with the Russian text rendered on it.
4. Copy the final translation to your clipboard.

lens_scan "path/to/manga.jpg" ja -t ru -to "translated_manga.png" -sx

6. Process an Image from a URL as a Single Line

Fetches an image directly from a URL and joins all recognized text into one continuous line, removing any line breaks.

lens_scan "https://i.imgur.com/VPd1y6b.png" en --ocr-single-line

7. Use a SOCKS5 Proxy

All requests to the Google API will be routed through the specified proxy server, which is useful for privacy or bypassing region restrictions.

lens_scan "image.png" --proxy "socks5://127.0.0.1:9050"

Basic Example (Full Text)

import asyncio
from chrome_lens_py import LensAPI

async def main():
    # Initialize the API. You can pass a proxy, region, etc. here.
    # By default, an API key is not required.
    api = LensAPI()

    image_source = "path/to/your/image.png" # Or a URL, PIL Image, NumPy array

    try:
        # Process the image and get a single string of text
        result = await api.process_image(
            image_path=image_source,
            ocr_language="ja",
            target_translation_language="en"
        )

        print("--- OCR Text ---")
        print(result.get("ocr_text"))

        print("\n--- Translated Text ---")
        print(result.get("translated_text"))
        
    except Exception as e:
        print(f"An error occurred: {e}")

if __name__ == "__main__":
    asyncio.run(main())

Working with Different Image Sources

The process_image method seamlessly handles various input types.

from PIL import Image
import numpy as np

# ... inside an async function ...

# From a URL
result_url = await api.process_image("https://i.imgur.com/VPd1y6b.png")

# From a PIL Image object
with Image.open("path/to/image.png") as img:
    result_pil = await api.process_image(img)

# From a NumPy array (e.g., loaded via OpenCV)
with Image.open("path/to/image.png") as img:
    numpy_array = np.array(img)
    result_numpy = await api.process_image(numpy_array)

Getting Segmented Text Blocks

To get text segmented into logical blocks (like dialog bubbles in a comic), use the output_format='blocks' parameter.

import asyncio
from chrome_lens_py import LensAPI

async def process_comics():
    api = LensAPI()
    image_source = "path/to/manga.jpg"
    
    result = await api.process_image(
        image_path=image_source,
        output_format='blocks' # Get segmented blocks instead of a single string
    )

    # The result now contains a 'text_blocks' key
    text_blocks = result.get("text_blocks", [])
    print(f"Found {len(text_blocks)} text blocks.")

    for i, block in enumerate(text_blocks):
        print(f"\n--- Block #{i+1} ---")
        print(block['text'])
        # block also contains 'lines' and 'geometry' keys

asyncio.run(process_comics())

Getting Individual Lines and their Geometry

To get each recognized line of text as a separate item, use the output_format='lines' parameter.

import asyncio
from chrome_lens_py import LensAPI

async def process_document_lines():
    api = LensAPI()
    image_source = "path/to/document.png"
    
    result = await api.process_image(
        image_path=image_source,
        output_format='lines' # Get individual lines with their geometry
    )

    # The result now contains a 'line_blocks' key
    line_blocks = result.get("line_blocks", [])
    print(f"Found {len(line_blocks)} lines.")

    for i, line in enumerate(line_blocks):
        print(f"\n--- Line #{i+1} ---")
        print(f"Text: {line['text']}")
        print(f"Geometry: {line['geometry']}")

asyncio.run(process_document_lines())

Getting Fully Detailed Text Structures

To get a complete, nested structure of paragraphs, lines, and words with geometry at each level, use output_format='detailed'.

import asyncio
from chrome_lens_py import LensAPI

async def process_with_details():
    api = LensAPI()
    image_source = "path/to/document.png"
    
    result = await api.process_image(
        image_path=image_source,
        output_format='detailed' # Get the fully nested structure
    )

    # The result now contains a 'detailed_blocks' key
    detailed_blocks = result.get("detailed_blocks", [])
    print(f"Found {len(detailed_blocks)} detailed blocks.")

    for i, block in enumerate(detailed_blocks):
        print(f"\n--- Block #{i+1} ---")
        print(f"  Geometry: {block['geometry']}")
        for j, line in enumerate(block['lines']):
            print(f"    --- Line #{j+1}: '{line['text']}' ---")
            for k, word in enumerate(line['words']):
                 print(f"      - Word: '{word['text']}', Geometry: {word['geometry']}")

asyncio.run(process_with_details())

LensAPI Constructor

api = LensAPI(
    api_key: str = "YOUR_API_KEY_OR_DEFAULT",
    client_region: Optional[str] = None,
    client_time_zone: Optional[str] = None,
    proxy: Optional[str] = None,
    timeout: int = 60,
    font_path: Optional[str] = None,
    font_size: Optional[int] = None,
    max_concurrent: int = 5
)

process_image Method

result: dict = await api.process_image(
    image_path: Any,
    ocr_language: Optional[str] = None,
    target_translation_language: Optional[str] = None,
    source_translation_language: Optional[str] = None,
    output_overlay_path: Optional[str] = None,
    ocr_preserve_line_breaks: bool = True,
    output_format: Literal['full_text', 'blocks', 'lines', 'detailed'] = 'full_text'
)

• output_format: Controls the structure of the OCR output. 'full_text' (default) returns a single string in ocr_text. 'blocks' returns a list in text_blocks. 'lines' returns a list in line_blocks. 'detailed' returns a fully nested structure in detailed_blocks.
• ocr_preserve_line_breaks: If False and output_format is 'full_text', joins all OCR text into a single line.

The returned result dictionary contains:

• ocr_text (Optional[str]): The full recognized text (if output_format='full_text').
• text_blocks (Optional[List[dict]]): A list of segmented text blocks (if output_format='blocks'). Each block is a dict with text, lines, and geometry.
• line_blocks (Optional[List[dict]]): A list of individual text lines (if output_format='lines'). Each block is a dict with text and geometry.
• translated_text (Optional[str]): The translated text, if requested.
• word_data (List[dict]): A list of dictionaries for every recognized word with its geometry.
• detailed_blocks (Optional[List[dict]]): A list of fully structured text blocks (if output_format='detailed'). Each block contains lines, which in turn contain words, with geometry at every level.
• raw_response_objects: The "raw" Protobuf response object for further analysis.

Settings are loaded with the following priority: CLI Arguments > config.json File > Library Defaults.

config.json

A config.json file can be placed in your system's default config directory to set persistent options.

• Linux: ~/.config/chrome-lens-py/config.json
• macOS: ~/Library/Application Support/chrome-lens-py/config.json
• Windows: C:\Users\<user>\.config\chrome-lens-py\config.json

Example config.json

{
  "api_key": "OPTIONAL! If you don't know what this is, I don't recommend setting it here.",
  "proxy": "socks5://127.0.0.1:9050",
  "client_region": "DE",
  "client_time_zone": "Europe/Berlin",
  "timeout": 90,
  "font_path": "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
  "ocr_preserve_line_breaks": true
}