It's 2025, and converting files from one format to another should only take a few clicks. But it often becomes a whole lengthy process requiring uploads to unsecured online converting apps that can put your personal information at risk. Usually, this PDF conversion license is 99.99, but right now, it's down to 23.99 when you use code SAVE20 at checkout. PDF Converter Pro works with Microsoft Word, Excel, PowerPoint, Text, HTML, PNG, and JPG files. It even maintains your original layouts, images, and hyperlinks even after conversion without losing quality.

Electrocardiograms (ECGs) play a vital role in diagnosing cardiovascular diseases (CVDs), which remain one of the leading causes of mortality worldwide. The accurate interpretation of ECG signals is crucial for early detection and timely medical intervention. Recent advancements in deep learning have significantly improved ECG-based diagnosis, with models achieving cardiologist-level performance [1, 2, 3, 4, 5, 6, 7]. However, most of these approaches rely on high-quality digital ECG data, limiting their real-world applicability in clinical environments where scanned paper ECGs are still prevalent. Paper-based ECGs remain widely used due to historical adoption, cost-effectiveness, and compatibility with legacy healthcare systems. However, relying on scanned ECGs introduces new challenges, as they contain image-based artifacts such as noise, distortions, and variations in paper quality, which can affect automated diagnostic accuracy. Traditional binary classification (normal vs. abnormal) methods may not fully capture the complexity of cardiac conditions present in real-world ECGs. Therefore, multi-label classification, where multiple cardiac abnormalities are identified simultaneously, presents a more clinically relevant and challenging problem. Recent efforts in ECG analysis have explored deep neural networks, including AlexNet, VGG, ResNet, and Vision Transformers, for automated classification of ECGs.

Oracle character recognition-an analysis of ancient Chinese inscriptions found on oracle bones-has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field.

Data science is widely regarded as one of the most essential parts of any industry in today's marketplace, given the massive amounts of data that are produced. Data Science is growing enormously to occupy all the industries of the world in the current world. In this article, we will understand how data science is transforming the healthcare sector. We will understand various underlying concepts of data science, used in medicine and biotechnology. Medicine and healthcare are two of the most important parts of our human lives. Traditionally, medicine solely relied on the discretion advised by the doctors.

In the following paper, we have combined the various basic functionalities provided by the NumPy library and OpenCv library, which is an open source for Computer Vision applications, like conversion of colored images to grayscale, calculating threshold, finding contours and using those contour points to take perspective transform of the image inputted by the user, using Python version 3.7. Additional features include cropping, rotating and saving as well. All these functions and features, when implemented step by step, results in a complete scanning application. The applied procedure involves the following steps: Finding contours, applying Perspective transform and brightening the image, Adaptive Thresholding and applying filters for noise cancellation, and Rotation features and perspective transform for a special cropping algorithm. The described technique is implemented on various samples.

"Gone are the days of pure mathematical approaches to solve a vision problem, now that AI has made its foray" -- this could be one of the most misleading thoughts of a Deep Learning practitioner, oblivious of traditional computer vision techniques. If you are one among, then here is an attempt to make you think again. Many of the computer vision algorithms running on the edge uses traditional math, rather than compute and memory intensive neural nets. Consider, we have scanned images containing textual content. The below attempt is to address some classical problems in scanned images, viz.

"Gone are the days of pure mathematical approaches to solve a vision problem, now that AI has made its foray" -- this could be one of the most misleading thoughts of a Deep Learning practitioner, oblivious of traditional computer vision techniques. If you are one among, then here is an attempt to make you think again. Many of the computer vision algorithms running on the edge uses traditional math, rather than compute and memory intensive neural nets. Consider, we have scanned images containing textual content. The below attempt is to address some classical problems in scanned images, viz.