Websites can increase their security and prevent harmful Internet attacks by providing CAPTCHA verification for determining whether end-user is a human or a robot. Text-based CAPTCHA is the most common and designed to be easily recognized by humans and difficult to identify by machines or robots. However, with the dramatic advancements in deep learning, it becomes much easier to build convolutional neural network (CNN) models that can efficiently recognize text-based CAPTCHAs. In this study, we introduce an efficient CNN model that uses attached binary images to recognize CAPTCHAs. By making a specific number of copies of the input CAPTCHA image equal to the number of characters in that input CAPTCHA image and attaching distinct binary images to each copy, we build a new CNN model that can recognize CAPTCHAs effectively. The model has a simple structure and small storage size and does not require the segmentation of CAPTCHAs into individual characters. After training and testing the proposed CAPTCHA recognition CNN model, the achieved experimental results reveal the strength of the model in CAPTCHA character recognition.

Websites can improve their security and protect against harmful Internet attacks by incorporating CAPTCHA verification, which assists in distinguishing between human users and robots. Among the various types of CAPTCHA, the most prevalent variant involves text-based challenges that are intentionally designed to be easily understandable by humans while presenting a difficulty for machines or robots in recognizing them. Nevertheless, due to significant advancements in deep learning, constructing convolutional neural network (CNN)-based models that possess the capability of effectively recognizing text-based CAPTCHAs has become considerably simpler. In this regard, we present a CAPTCHA recognition method that entails creating multiple duplicates of the original CAPTCHA images and generating separate binary images that encode the exact locations of each group of CAPTCHA characters. These replicated images are subsequently fed into a well-trained CNN, one after another, for obtaining the final output characters. The model possesses a straightforward architecture with a relatively small storage in system, eliminating the need for CAPTCHA segmentation into individual characters. Following the training and testing of the suggested CNN model for CAPTCHA recognition, the experimental results demonstrate the model’s effectiveness in accurately recognizing CAPTCHA characters.

Image captioning has seen significant research efforts over the last decade. The goal is to generate meaningful semantic sentences that describe visual content depicted in photographs and are syntactically accurate. Many real-world applications rely on image captioning, such as helping people with visual impairments to see their surroundings. To formulate a coherent and relevant textual description, computer vision techniques are utilized to comprehend the visual content within an image, followed by natural language processing methods. Numerous approaches and models have been developed to deal with this multifaceted problem. Several models prove to be state-of-the-art solutions in this field. This work offers an exclusive perspective emphasizing the most critical strategies and techniques for enhancing image caption generation. Rather than reviewing all previous image captioning work, we analyze various techniques that significantly improve image caption generation and achieve significant performance improvements, including encompassing image captioning with visual attention methods, exploring semantic information types in captions, and employing multi-caption generation techniques. Further, advancements such as neural architecture search, few-shot learning, multi-phase learning, and cross-modal embedding within image caption networks are examined for their transformative effects. The comprehensive quantitative analysis conducted in this study identifies cutting-edge methodologies and sheds light on their profound impact, driving forward the forefront of image captioning technology.

Image captioning has gained increasing attention in recent years. Visual characteristics found in input images play a crucial role in generating high-quality captions. Prior studies have used visual attention mechanisms to dynamically focus on localized regions of the input image, improving the effectiveness of identifying relevant image regions at each step of caption generation. However, providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these features. Consequently, this leads to enhanced captioning network performance. In light of this, we present an image captioning framework that efficiently exploits the extracted representations of the image. Our framework comprises three key components: the Visual Feature Detector module (VFD), the Visual Feature Visual Attention module (VFVA), and the language model. The VFD module is responsible for detecting a subset of the most pertinent features from the local visual features, creating an updated visual features matrix. Subsequently, the VFVA directs its attention to the visual features matrix generated by the VFD, resulting in an updated context vector employed by the language model to generate an informative description. Integrating the VFD and VFVA modules introduces an additional layer of processing for the visual features, thereby contributing to enhancing the image captioning model’s performance. Using the MS-COCO dataset, our experiments show that the proposed framework competes well with state-of-the-art methods, effectively leveraging visual representations to improve performance. The implementation code can be found here: (accessed on 30 July 2024).

Image captioning, the task of generating descriptive sentences for images, has advanced significantly with the integration of semantic information. However, traditional models still rely on static visual features that do not evolve with the changing linguistic context, which can hinder the ability to form meaningful connections between the image and the generated captions. This limitation often leads to captions that are less accurate or descriptive. In this paper, we propose a novel approach to enhance image captioning by introducing dynamic interactions where visual features continuously adapt to the evolving linguistic context. Our model strengthens the alignment between visual and linguistic elements, resulting in more coherent and contextually appropriate captions. Specifically, we introduce two innovative modules: the Visual Weighting Module (VWM) and the Enhanced Features Attention Module (EFAM). The VWM adjusts visual features using partial attention, enabling dynamic reweighting of the visual inputs, while the EFAM further refines these features to improve their relevance to the generated caption. By continuously adjusting visual features in response to the linguistic context, our model bridges the gap between static visual features and dynamic language generation. We demonstrate the effectiveness of our approach through experiments on the MS-COCO dataset, where our method outperforms state-of-the-art techniques in terms of caption quality and contextual relevance. Our results show that dynamic visual-linguistic alignment significantly enhances image captioning performance.