CAPTCHA is an acronym for Completely Automated Public Turing test to tell Human and Computer Apart. The main purpose of CAPTCHA is to differentiate between human and automated machine during online transaction. Text, image, audio and video are types of CAPTCHAs. However, text-based CAPTCHAs are available in the market in different languages i.e., English, Arabic, Urdu and Chinese but accented character-based text CAPTCHA system namely NAIJACAPTCHA is a newly introduce text-based CAPTCHA developed using Latin characters and accented characters from two Nigerian language: Yorùbá and Igbo. The usability of sixteen accented character-based CAPTCHAs was tested to see if they were suitable for human usage. The usability performance was measured using response time, solving time, accuracy, and success rate. A total of two hundred and twenty-two participants were selected for the study, and 1108 CAPTCHA codes were generated. The response time for Text Distortion with Coloured Background was the fastest, with 1.18×103 ms, while Coloured Texts with No Background (CTNB) had the least response time of 1.09 ms. With a solving time of 2.52×104 ms, Character Fragmentation with No Background was the fastest. The result showed that CTBN's response and problem-solving time is highly promising; as a result, its website application for authentication during online transactions to distinguish between humans and machines will be simple for human beings to solve and user requests will also be swiftly attended to. Lastly, the security aspect of the developed NAIJACAPTCHA will be looked into determine its vulnerability.

The advent of the study of Scene Text Detection and Recognition has exposed some significant challenges text recognition faces, such as blurred text detection. This study proposes a comparative model for detecting blurred text in wild scenes using independent component analysis (ICA) and enhanced genetic algorithm (E-GA) with support vector machine (SVM) and k-nearest neighbors (KNN) as classifiers. The proposed model aims to improve the accuracy of blurred text detection in challenging environments with complex backgrounds, noise, and illumination variations. The proposed model consists of three main stages: preprocessing, feature extraction, and classification. In the preprocessing stage, the input image is first preprocessed to remove noise and enhance edges using a median filter and a Sobel filter, respectively. Then, the blurred text regions are extracted using the Laplacian of Gaussian (LoG) filter. In the feature extraction stage, ICA is used to extract independent components from the blurred text regions. The extracted components are then fed into an E-GA-based feature selection algorithm to select the most discriminative features. The E-GA simply fine tunes the selection functionalities of the traditional GA using a bird approach. The selected features are then normalized and fed into the SVM and KNN classifiers. Experimental results on a benchmarking dataset (ICDAR 2019 LSVT) shows that the model outperforms state-of-the-art methods in terms of detection accuracy, precision, recall, and F1-score. The proposed model achieves an overall accuracy of 95.13% for SVM and 88.69% for KNN, which is significantly higher than the already existing methods which for SVM is 93%. In conclusion, the proposed model provides a promising approach for detecting blurred text in wild scenes. The combination of ICA, E-GA, and SVM/KNN classifiers enhances the robustness and accuracy of the detection system, which can be beneficial for a wide range of applications, such as text recognition, document analysis, and security systems.