FunCaptchaClassification: FunCaptcha image recognition

FunCaptchaClassification: FunCaptcha image recognition

This object does not return a RESPONSE, only the results of image recognition.

Please ensure your screenshot is complete, as partial images may affect recognition accuracy.

Create Task

Create recognition task using the method of Create Task.

Request node: INTERNATIONAL NODE https://api.yescaptcha.com CHINA NODE https://cn.yescaptcha.com

Method address:: https://api.yescaptcha.com/createTask

Request format:POST application/json

Object structure

Attribute

Type

Required

Description

Attribute

Type

Required

Description

type

string

Yes

FunCaptchaClassification 3 POINTS

image

string

Yes

Base64-encoded image (screenshot of the 6-grid section ONLY - exclude titles or other content. Submit ONLY the image!)

question

String

Yes

Problem name: Provide the full name in English (case-sensitive), e.g. "Pick the lion". Only English is supported (translate other languages to English if needed).

The server automatically determines the image type based on the problem name - please ensure it is correct.

For all image types and their corresponding English problem names, refer to the list at the end of this document.

Request Example

{ "clientKey": "cc9c18d3e263515c2c072b36a7125eecc078618f", // Your clientkey "task": { "type": "FunCaptchaClassification", "image": "data:image/jpeg;base64,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", // English only (case-sensitive). Translate other languages to English if needed. // The server determines image types based on the problem name - ensure accuracy. "question": "Pick the bread" } }

Response Example

{ "errorId": 0, "errorCode": "", "status": "ready", "solution": { "objects": [4], // Target position: 5th image (0-based index) "labels": [ "parrot", "panda", "octopus", "owl", "bread", "dog" ] }, "taskId": "04a3fa76-11a0-11ed-a58c-0ef20f419382" }

 

Image Format and Problem Description:

If you encounter a new type, please contact customer support to add support for it.

PS If you encounter a vertical 6-grid layout, upload the original image directly - the server can automatically recognize it.

 

Type

English

Chinese

Description

Type

English

Chinese

Description

Pick the bread

选出面包

 

目前有52种不同的分类,请直接传完整问题

Pick one square that shows two identical objects

选出两个相同的图形

 

还有一种3个黑白扭曲变形图标,请看下面。

Pick one square that shows two identical objects

选出两个相同的图形

问题与上面的相同,区别是黑暗版本的尺寸为450*300,请确保图片尺寸正确

Pick the penguin

选出企鹅

 

Pick the shadow with a different object silhouette

选择具有不同对象轮廓的阴影

 

Pick the dice pair with the same icon facing up

选择相同图标的骰子对

 

Pick the dice pair whose top sides add up to 14

选择顶边加起来为 14 的骰子对

准确率98.8%,更新于2023/04/20

Pick the matching cards

选择匹配的卡片

 

 

Pick the animal looking at the shape that matches the shape it's standing on

选择动物看向的形状与它站立的形状相匹配的

 

Pick one square that shows three of the same object.

选出三个相同图形的方块

 

Pick the mouse that can reach all the cheese in the maze

选出能够拿到迷宫里所有奶酪的老鼠

 

Pick the mouse that can't reach the cheese

选出不能够拿到迷宫里所有奶酪的老鼠

准确率98.19%,更新于2023/06/20

Pick the image where the darts add up to 10

选择飞镖加起来为 10 的图像

 

Pick the image that is the correct way up

选择正确向上的图像

 

Pick the dice pair whose top sides add up to 5

选择顶边加起来为 5 的骰子对

(5/6/7/8/9都有)

Pick the wrong shadow

选择错误的阴影

 

Select the animal with the wrong head

选择头部错误的动物

 

Pick the shadow that mathes the icons at the top of the image

选择与图像顶部的图标匹配的阴影

 

Pick the image of 2 striped shapes

选择2个条纹形状的图像

 

Pick the image of 2 checkered shapes

选择 2 个方格形状的图像

 

Pick the spiral galaxy

选择螺旋星系

Use the arrows to rotate the animal to face in the direction of the hand

选择与手指方向相同的动物图片

准确率96.06%,更新于2023/04/25

Change the dice until the count matches the image on the left

相加等于目标点数

 

准确率98.52%,更新于2023/06/18

 

Use the arrows to change the number of objects until it matches the left image

选择物体数量正确的图片

准确率99.31%,更新于2023/06/12

Using the arrows, move the person to the indicated seat

选择坐在指定座位的人物图片

准确率98.21%,更新于2023/06/04

Use the arrows to move the person to the spot indicated by the cross

选择站在指示位置上的人

准确率99.47%,更新于2023/05/24

Use the arrows to move the person to the icon indicated by the colored circle

选择站在指示位置上的人

准确率97.37%,更新于2023/05/15

 

Pick the image where all animals are walking in the same direction as the arrow

选出所有动物都沿着箭头方向行走的图像

 

 

Select the image with the icon order Chair then Fence

选择先椅子再围栏的图片

 

 

Using the arrows, connect the same two icons with the dotted line as shown on the left

选择虚线连接相同的两个图标

 

 

Select the image where the total fingers add up to 4

选择总共有4根手指的图片

准确率100.00%,更新于2023/04/16

Select the image where the total fingers add up to 3

选择总共有3根手指的图片

准确率100.00%,更新于2023/06/16

 

Use the arrows to place the train on the coordinate point shown in the picture on the left

选择停在指定位置的火车

准确率98.06%,更新于2023/06/07

 

use the arrows to move the train to the coordinates shown in the picture on the left

选择停在指定位置的火车

准确率99.89%,更新于2023/06/19

Use the arrows to move the train into the position indicated in the left image

选择停在指定位置的火车

 

 

Pick any square

选择任何正方形

 

 

Select an image of three circles in any direction on a straight line

选取任意方向上三个圆圈在一条直线上的

图片

 

 

Pick the image with 3 crosses in a row in any direction

选择任何方向连续 3 个叉的图像

 

 

Pick the image where the number matches the amount of animals

选择数字与动物数量相匹配的图像

 

 

Use the arrows to rotate the animal with the same icon to face where the hand is pointing

选择具有相同图标的动物面向手指方向的图片

准确率94.45%,更新于2023/06/13

Use the buttons to place the indicated car, in the correct position in the race

选择在比赛中正确位置的汽车

 

 

Pick the distorted object

选择扭曲的物体

准确率67.00%,更新于2023/06/09

Pick the object that is not distorted

选择没有扭曲的物体

准确率54.00%,更新于2023/06/09

Select the animal with the wrong head

选择头和身体匹配错误的动物

准确率100.00%,更新于2023/06/14

更多未列出类型,

请直接提交英文问题。

如果是截图,请仔细检查您上传的图片是否完整,如果不完整会影响识别效果,如下图:

传这种
不要传这种

 

使用方法:

一、如果您是移动端调用,直接截图下方红色框框区域图片即可,一共六张,拼成一个数组提交到后端进行识别

请注意问题一定正确,下面附所有已支持的问题

请注意:如果是截图拼成数组,请确保不少于6张图片

请注意:请确保单张图片大小不超过120Kb,超过会报错,推荐尺寸386x200

请注意:请传下图中红色部份的内容,不要多传其他区域内容

{ "clientKey": "cc9c18d3e263515c2c072b36a7125eecc078618f", // 填你的密钥 "task": { "type": "FunCaptchaClassification", "image": [ // 六张截图 "data:image/jpeg;base64,/9j/2wCEAAoHB...", "data:image/jpeg;base64,/9j/2wCEAAoHB...", …… "question": "Use the arrows to rotate the animal to face in the direction of the hand" } }

二、如果您是PC端调用,可以直接在网页上获取原图,如下所示,按默认的1张图片提交方式,正常提交到后端即可。

请注意:如果是提交一张原图,请确保图片宽度不小于1200px

 

注意,如果在被风控的情况下,可能会遇见超过6张图片的情况,如果是截图请截到每一张,如果是网页还是一样传原始图片即可。

12张

20张

请确保问题正确,其他语言请转换为下列英文问题(请勿自行翻译)

直接获取网页上的问题提交也是可以的。

如果出现新样式我们可能不会同步在文档上,您直接获取图片和问题提交即可。

 

针对新出现的点击样式,我们已经增加了识别

目前处于样本积攒阶段,识别速度与准确率会略差,

当样本数量足够时会训练模型提高准确率与识别速度

 

 

附注:

Pick the xxx的50个问题列表