Recaptcha2 Image Recognition API Integration Instructions

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.

Application Process

To use the Recaptcha2 image recognition API, first go to the Ace Data Cloud Console to obtain your API Token for future use.

If you are not logged in or registered, you will be automatically redirected to the login page inviting you to register and log in, and after completion, you will be automatically returned to the current page.

One API Token can call all services on the platform without needing to apply separately for each service. The first application will grant a free quota for a trial experience; when the quota is insufficient, you can recharge the general balance in the console.

📘 Complete Documentation: Recaptcha2 Image Recognition API →

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 of 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 in the following text. First, we need to pass a simple image field, 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 question related to the verification image. We only provide the following content table for reference:

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"
}

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 filled in as application/json, which is JSON format.
  • authorization: the key to call the API, which can be selected directly 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:

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 in 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.
  • started_at, finished_at: the time when this request started processing and produced results (ISO-8601 UTC).
  • elapsed: the total time taken for this processing (in seconds).

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:

It can be seen that the areas start from 0. From the result in objects, we obtained 2, 5, and 7, and 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)

Asynchronous Mode (async)

By default, the API is synchronous and blocking: a request will wait until the recognition result is processed before returning. If you are doing multi-solver rotation and want to "immediately get the task_id after submitting the task, schedule other solvers, and come back later for the result," you can pass async: true in the request body.

After passing async: true, the interface will immediately return a task_id without blocking:

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...",
  "async": true
}'

{
  "task_id": "61138bb6-19aa-11ec-a9c8-0242ac110002",
  "trace_id": "2efa9340-b21b-4e26-9e14-4aac95f343ab"
}

Then use the task_id to poll POST /captcha/tasks (recommended every 3-5 seconds) to get the result:

curl -X POST 'https://api.acedata.cloud/captcha/tasks' \
-H 'accept: application/json' \
-H 'authorization: Bearer {token}' \
-H 'content-type: application/json' \
-d '{
  "task_id": "61138bb6-19aa-11ec-a9c8-0242ac110002"
}'

During processing, it will return status: processing:

{ "success": true, "task_id": "61138bb6-19aa-11ec-a9c8-0242ac110002", "status": "processing" }

Once processing is complete, it will return status: ready and the recognition result solution (the field structure is completely consistent with the synchronous mode):

{
  "success": true,
  "task_id": "61138bb6-19aa-11ec-a9c8-0242ac110002",
  "status": "ready",
  "solution": {
    "size": 300,
    "label": "/m/01pns0",
    "objects": [2, 5, 7],
    "type": "multi"
  }
}

Billing explanation: In asynchronous mode, creating tasks and polling "processing" do not incur charges; only when successfully obtaining the recognition result is there a one-time charge (consistent with the price of synchronous mode). Therefore, canceling unfinished tasks during rotation will not incur costs. /captcha/tasks is applicable to all captcha interfaces (token and recognition series), and you can poll with the same task_id.

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.

Error Response Example

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

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 captcha image, ultimately returning the coordinates of the small images that need to be clicked to complete the verification. We hope this document helps you better integrate and use the API. If you have any questions, please feel free to contact our technical support team.