Audio Captcha Recognition Using RastaPLP Features by SVM

TL;DR

This paper presents a method for recognizing audio CAPTCHAs by extracting RastaPLP features and classifying them with SVM, aiming to improve automated human verification systems against bots.

Contribution

It introduces a novel approach combining RastaPLP feature extraction with SVM classification specifically for audio CAPTCHA recognition.

Findings

High accuracy in digit recognition from noisy audio CAPTCHAs

Effective feature extraction method for audio CAPTCHA analysis

Potential to enhance automated CAPTCHA solving systems

Abstract

Nowadays, CAPTCHAs are computer generated tests that human can pass but current computer systems can not. They have common usage in various web services in order to be able to detect a human from computer programs autonomously. In this way, owners can protect their web services from bots. In addition to visual CAPTCHAs which consist of distorted images, mostly test images, that a user must write some description about that image, there are a significant amount of audio CAPTCHAs as well. Briefly, audio CAPTCHAs are sound files which consist of human sound under heavy noise where the speaker pronounces a bunch of digits consecutively. Generally, in those sound files, there are some periodic and non-periodic noises to get difficult to recognize them with a program but not for a human listener. We gathered numerous randomly collected audio file to train and then test them using our SVM algorithm to be able to extract digits out of each conversation.