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This article will introduce a Recaptcha2 image recognition API integration, which can identify the content entered by the user and the Recaptcha2 verification image, and finally return the coordinates of the small images that need to be clicked to complete the verification.

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Application Process

To use the API, you need to first apply for the corresponding service on the Recaptcha2 Image Recognition API page. After entering the page, click the “Acquire” button, as shown in the image below: If you are not logged in or registered, you will be automatically redirected to the login page inviting you to register and log in. After logging in or registering, you will automatically return to the current page. There will be a free quota granted upon your first application, allowing you to use the API for free.

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Basic Usage

First, let’s understand the basic usage. We need to capture the Recaptcha2 verification image from the website. The URL of the example website is: https://www.google.com/recaptcha/api2/demo, and the specific page is shown in the image below:

We need to click the checkbox for the verification code to display the verification image. In the image above, the yellow arrow points to a piece of text, which is the value of question mentioned later. First, we need to simply pass a field called image, which is the specific Recaptcha2 verification image, indicated by the red arrow in the image above. The image must be scaled to standard sizes (100x100, 300x300, 450x450) so that the service can determine the image type. You need to compress the image yourself; this article recommends a compression website where you can resize and compress the image. The result after compression is shown in the image below: You also need to input the recognition content parameter related to the verification image, question. We have provided the following content table for reference:

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Chinese Content Table

{
  "/m/0pg52": "出租车",
  "/m/01bjv": "巴士",
  "/m/02yvhj": "校车",
  "/m/04_sv": "摩托车",
  "/m/013xlm": "拖拉机",
  "/m/01jk_4": "烟囱",
  "/m/014xcs": "人行横道",
  "/m/015qff": "红绿灯",
  "/m/0199g": "自行车",
  "/m/015qbp": "停车计价表",
  "/m/0k4j": "汽车",
  "/m/015kr": "桥",
  "/m/019jd": "船",
  "/m/0cdl1": "棕榈树",
  "/m/09d_r": "山",
  "/m/01pns0": "消防栓",
  "/m/01lynh": "楼梯"
}

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English Content Table

{
  "/m/0pg52": "taxis",
  "/m/01bjv": "bus",
  "/m/02yvhj": "school bus",
  "/m/04_sv": "motorcycles",
  "/m/013xlm": "tractors",
  "/m/01jk_4": "chimneys",
  "/m/014xcs": "crosswalks", // pedestrian crossings is the same
  "/m/015qff": "traffic lights",
  "/m/0199g": "bicycles",
  "/m/015qbp": "parking meters",
  "/m/0k4j": "cars",
  "/m/015kr": "bridges",
  "/m/019jd": "boats",
  "/m/0cdl1": "palm trees",
  "/m/09d_r": "mountains or hills",
  "/m/01pns0": "fire hydrant",
  "/m/01lynh": "stairs"
}
From the above, we can set the parameter question to the fire hydrant corresponding to /m/01pns0, with the specific content as follows:

Here we can see that we have set the Request Headers, including:
  • accept: the format of the response result you want to receive, here it is filled as application/json, which means JSON format.
  • authorization: the key to call the API, which can be directly selected after application.
Additionally, we set the Request Body, including:
  • image: the Base64 encoded verification image.
  • question: the question ID, please refer to the table, starting with /m/.
After selection, you can find that the corresponding code is also generated on the right side, as shown in the image below:

Click the “Try” button to test, as shown in the image above, and we get the following result: 
{
  "solution": {
    "size": 300,
    "label": "/m/01pns0",
    "confidences": [
      0,
      0.0007,
      1,
      0.0003,
      0.0046,
      1,
      0,
      1,
      0
    ],
    "objects": [
      2,
      5,
      7
    ],
    "type": "multi"
  }
}
The returned result contains multiple fields, described as follows:
  • solution, the verification result after processing the Recaptcha2 verification image task.
    • size, the size of the Recaptcha2 verification image.
    • label, the content recognized from the Recaptcha2 verification image.
    • confidences, the confidence levels of the recognized areas in the Recaptcha2 verification image, with areas starting from 0.
    • objects, the areas that meet the recognized content after recognition of the Recaptcha2 verification image, with areas starting from 0.
    • type, the type of the Recaptcha2 verification image task, which is multi when there are multiple areas.
We can see that we have obtained the verification result for processing the Recaptcha2 verification image. We first divide the verification image into areas, as shown in the image below:

The areas start from 0, and from the result in objects, we obtained 2, 5, and 7. We only need to simulate clicking on these three areas of the verification code to pass the verification. Additionally, if you want to generate the corresponding integration code, you can directly copy the generated code, for example, the CURL code is as follows: 
curl -X POST 'https://api.acedata.cloud/captcha/recognition/recaptcha2' \
-H 'accept: application/json' \
-H 'authorization: Bearer {token}' \
-H 'content-type: application/json' \
-d '{
  "question": "/m/01pns0",
  "image": "iVBORw0KGgoAAAANSUhEUgAAASoAAAEsCAIAAAD7AWllAAAAAX..."
}'
The Python integration code is as follows: 
import requests

url = "https://api.acedata.cloud/captcha/recognition/recaptcha2"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json"
}

payload = {
    "question": "/m/01pns0",
    "image": "iVBORw0KGgoAAAANSUhEUgAAASoAAAEsCAIAAAD7AWllAAAAAX..."
}

response = requests.post(url, json=payload, headers=headers)
print(response.text)

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Error Handling

When calling the API, if an error occurs, the API will return the corresponding error code and message. For example:
  • 400 token_mismatched: Bad request, possibly due to missing or invalid parameters.
  • 400 api_not_implemented: Bad request, possibly due to missing or invalid parameters.
  • 401 invalid_token: Unauthorized, invalid or missing authorization token.
  • 429 too_many_requests: Too many requests, you have exceeded the rate limit.
  • 500 api_error: Internal server error, something went wrong on the server.

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Error Response Example

{
  "success": false,
  "error": {
    "code": "api_error",
    "message": "fetch failed"
  },
  "trace_id": "2cf86e86-22a4-46e1-ac2f-032c0f2a4e89"
}

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Conclusion

Through this document, you have learned how to use the Recaptcha2 image recognition API to allow users to input the recognized content and the Recaptcha2 verification code image, and finally return the coordinates of the small image that needs to be clicked to complete the verification. We hope this document can help you better integrate and use this API. If you have any questions, please feel free to contact our technical support team.