Cap.guruRecognizing Text-Based CAPTCHAs from Images
Automated solving of image-based text CAPTCHAs: submit an image — receive the recognized text. Fast, accurate, and via a simple API.
Get StartedWhat Is an Image CAPTCHA
An image CAPTCHA is a classic challenge-response test where the user is presented with an image containing distorted text, digits, or a combination of both. The task is to correctly read the characters and enter them into an input field. These CAPTCHAs are still widely used on forums, registration pages, payment systems, and government portals.
Despite the emergence of more sophisticated protection mechanisms, text-based CAPTCHAs remain among the most common. They may include noise, strikethrough lines, overlapping characters, varying fonts, and warped text — all of which complicate automated recognition.
Letters & Digits
Latin, Cyrillic, digits, and mixed character sets — the service recognizes any symbol combinations.
Distortions & Noise
Strikethrough lines, wavy patterns, noisy backgrounds — the algorithm handles all types of interference.
Colored & Complex
Multi-colored backgrounds, gradients, overlapping elements — all recognized with high accuracy.
Supported Image Types
The service accepts any graphical CAPTCHA that requires recognizing text in an image and converting it to a character string. Image CAPTCHA solving is supported for all major types:
Standard
Conventional text CAPTCHAs with letters and digits on a colored background.
Mathematical
CAPTCHAs with arithmetic expressions: "3 + 7 = ?", "12 − 4 = ?".
Cyrillic
Russian, Ukrainian, and other Cyrillic characters in the image.
Noisy
Heavily distorted images with lines, dots, and overlapping characters.
How Image-to-Text Recognition Works
The process is straightforward: you send the CAPTCHA image to the server and receive the recognized text in response. The image can be submitted as a Base64-encoded string or via a direct URL. The server processes the image using neural networks and OCR algorithms, then returns the resulting text string.
Obtain the CAPTCHA Image
Download the CAPTCHA image from the page or retrieve its URL.
Submit for Recognition
Send the image to the API via a POST request — as a Base64 string or a file URL.
Receive the Text
The server recognizes the characters and returns the extracted text (image to text). Average response time is 1–3 seconds.
Insert the Result
Paste the returned text into the input field on the target site and submit the form — CAPTCHA solved.
💡 Pre-built modules and libraries for popular programming languages are available for API integration. See the documentation for details.
Browser Extension
In addition to the API, a browser extension is available for Chrome and Firefox that solves text CAPTCHAs directly on the page — no coding required. The extension automatically detects the CAPTCHA image, submits it for recognition, and inserts the resulting text into the input field. Setup takes a couple of minutes: just install the extension from your browser's web store and enter your API key.
Chrome Extension
Extension for Google Chrome — automatic CAPTCHA text recognition and input on any website.
Firefox Add-on
Add-on for Mozilla Firefox — the same image-to-text functionality directly in the browser.
Service Advantages
Solving text CAPTCHAs via API is fast, reliable, and affordable. The service is suitable for both individual requests and large-scale automation.
1–3 sec Response Time
Average recognition time is 1 to 3 seconds. Ideal for real-time workflows.
High Accuracy
Neural networks and OCR algorithms deliver recognition accuracy above 95% on standard CAPTCHAs.
Low Cost
Text CAPTCHAs are the most affordable task type. You only pay for successful recognitions.
Easy Integration
Ready-made libraries for Python, Node.js, PHP, C#, and other languages. Integration in 5 minutes.
Best Practices
For maximum image CAPTCHA recognition accuracy, follow a few guidelines. Submit images at their original quality — do not compress or resize the image before sending. If you know the CAPTCHA format in advance, specify additional parameters in the request — this significantly improves the success rate.
Detailed integration examples in various programming languages are available in our documentation.
