How to Build a High‑Accuracy Image CAPTCHA Recognition System with CNNs

Introduction

Snowball shares a hands‑on project that covers the entire pipeline of common CAPTCHA labeling and recognition, detailing the inspiration, requirement analysis, and implementation ideas.

Background

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is used to distinguish humans from bots. Image CAPTCHAs are the most prevalent type, and this article focuses on recognizing them.

Motivation and History

The idea originated during university years when the author tried to bypass a course‑selection system’s CAPTCHA but lacked effective machine‑learning tools. After several years of professional experience and advances in AI, the author revisited the problem, aiming to achieve high‑accuracy recognition with a CNN model.

After months of development, the project achieved over 97% character‑level accuracy on a dataset of 500 training images.

Project Resources

[Java Backend – General CAPTCHA Labeling System] https://gitee.com/snowball2dev/DataMarkService
[Vue Admin Template – General CAPTCHA Labeling System] https://gitee.com/snowball2dev/DataMarkService-Vue
[Python Image CAPTCHA Recognition Model] https://gitee.com/snowball2dev/VerifyCodeRecognize-Python
[Online Demo of the Labeling System] http://139.9.191.103:8084/

Requirement Analysis

Searching GitHub reveals many open‑source projects, but most either suffer from low accuracy (<70% per character) or require extensive manual labeling.

Two main implementation strategies are identified:

1. No Image Segmentation – Directly feed whole CAPTCHA images into a CNN for feature extraction. Simple and generic but needs large labeled datasets.

2. Character Segmentation – Preprocess images to isolate individual characters, then train a classifier on the smaller pieces. This reduces model complexity and labeling effort but is less universal.

An additional approach using OpenCV contour detection and SIFT feature matching was tried but proved unsuitable due to high variability in character generation.

Implementation Approach

The author chose the segmentation‑based method and organized the workflow into four steps:

Data Collection & Preprocessing : Batch download images via HTTP, apply OpenCV preprocessing.

Data Annotation : Develop a GUI for manual labeling; later augment the dataset with model predictions.

CNN Model Training : Use PyTorch on Windows (CPU/GPU) after studying calculus, linear algebra, and probability fundamentals.

Deployment : Deploy the Vue front‑end, Java back‑end, and Python model on a Linux server.

Visual Overview

Conclusion

The article summarizes the background knowledge of common CAPTCHA labeling and recognition, explains the inspiration, requirement analysis, and detailed implementation steps, and encourages readers to try the approach themselves.