Text-based Image Research Articles

A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks.

A Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA) is used in web systems to secure authentication purposes; it may break using Optical Character Recognition (OCR) type methods. CAPTCHA breakers make web systems highly insecure. However, several techniques to break CAPTCHA suggest CAPTCHA designers about their designed CAPTCHA’s need improvement to prevent computer vision-based malicious attacks. This research primarily used deep learning methods to break state-of-the-art CAPTCHA codes; however, the validation scheme and conventional Convolutional Neural Network (CNN) design still need more confident validation and multi-aspect covering feature schemes. Several public datasets are available of text-based CAPTCHa, including Kaggle and other dataset repositories where self-generation of CAPTCHA datasets are available. The previous studies are dataset-specific only and cannot perform well on other CAPTCHA’s. Therefore, the proposed study uses two publicly available datasets of 4- and 5-character text-based CAPTCHA images to propose a CAPTCHA solver. Furthermore, the proposed study used a skip-connection-based CNN model to solve a CAPTCHA. The proposed research employed 5-folds on data that delivers 10 different CNN models on two datasets with promising results compared to the other studies.

텍스트 기반 의료영상 검색의 최근 발전

텍스트 기반 의료영상 검색의 최근 발전

Medical Image Retrieval Algorithm Based on Content

With the rapid development of science and technology and the improving medical service, medical image's role in clinical diagnosis and treatment becomes increasingly prominent. It has become a high-profile task to help the doctors pick out desired targets from massive medical images. Currently, techniques of text-based medical image retrieval have failed to meet the need of massive medical image retrieval. On the other hand, techniques of content-based medical image retrieval are already established and hold vast research potential. Starting with the introduction of matured techniques of medical image retrieval, the paper expounds on the evaluation criteria of the effectiveness of such techniques, then on the modified text-and-content-based medical image retrieval algorithm. The last part is the verification of the research conclusion with contrast experiments illustrated by the sample figures.

An Index Based on the Method of Inertial Parameters Parts

Since the information contained in the three-dimensional model is very rich, involving many factors shape contour, texture, color, texture and other graphic images, as well as traditional as it will no longer rely on text-based entry staff image search technology text description. Therefore, the direct retrieval of the three-dimensional model is very complex, so this paper proposes a centroid as the origin of coordinates to strike inertial parameters of mechanical parts as the retrieval feature vector, to establish parameters for the index of the element of inertia characteristics of the table, enabling the retrieval development of the system.

An effective World Wide Web image search engine

Effective World Wide Web image retrieval systems are required to locate relevant images as more and more images are used in HTML documents. There are two general approaches to image retrieval: one is based on text descriptions and the other on image contents (low-level features, such as colour and texture). They have their respective strengths and weaknesses. In this paper, we describe how to improve text-based and content-based image retrieval techniques and to integrate these two types of techniques in one system to take advantage of their complementing strengths. Our experimental results show that the integrated approach has higher retrieval performance than either the text-based or the content-based techniques used by themselves.