In this tutorial, you will learn how to OCR a document, form, or invoice using Tesseract, OpenCV, and Python. On the left, we have our template image (i.e., a form from the United States Internal Revenue Service). The middle figure is our input image that we wish to align to the template (thereby allowing us to match fields from the two images together). And finally, the right shows the output of aligning the two images together. At this point, we can associate text fields in the form with each corresponding field in the template, meaning that we know which locations of the input image map to the name, address, EIN, etc. fields of the template: Knowing where and what the fields are allows us to then OCR each individual field and keep track of them for further processing, such as automated database entry.
For years, the lidar business has had a lot of hype but not a lot of hard numbers. Dozens of lidar startups have touted their impressive technology, but until recently it wasn't clear who, if anyone, was actually gaining traction with customers. This summer, three leading lidar makers have done major fundraising rounds that included releasing public data on their financial performance. The latest lidar maker to release financial data is Ouster, which announced a $42 million fundraising round in a Tuesday blog post. That blog post also revealed a striking statistic: the company says it now has 800 customers.
Early this year, Apple officially finished rebuilding Apple Maps in the US. Today, 9to5Mac shared some exclusive details about how Apple operates its mapping vehicles and how it manages the data they capture. According to internal materials seen by 9to5Mac, Apple's 3D Vision team cruises around in a fleet of white Subaru Imprezas. The vehicles are equipped with high-res cameras and LiDAR scanners that combine computer vision and machine learning data to generate 3D images on Apple Maps. All of that data is processed by a 2013 Mac Pro and stored in four SSDs with 4TB of storage each.
In this tutorial, we will see how to Create TensorFlow Image Detection In Angular 9. Creating a small functionality like an AI – Image Detection becomes so easy by using the TensorFlow modules. TensorFlow can be used in the web application by using the JS library of the TensorFlow. For this Demonstration, we will be using the Image Classification module. Using Image Classification Module we can detect any image containing people, activities, animals, plants, and places. We will use this module in the Angular 9 application.
How can your phone determine what an object is just by taking a photo of it? How do social media websites automatically tag people in photos? This is accomplished through AI-powered image recognition and classification. The recognition and classification of images is what enables many of the most impressive accomplishments of artificial intelligence. Yet how do computers learn to detect and classify images?
In this special guest feature, Hari Miriyala, VP Software Engineering at cPacket Networks, discusses how many enterprises are considering or deploying AI/ML tools to make their IT team more efficient, reduce troubleshooting time, or improve their organization's security. But without the right foundation of accurate, precise and consistent input data, this move to AIOps provides little value. Mr. Miriyala has over two decades of technical leadership and engineering expertise from SONET to ROADM/DWDM-based optical networking to multi-service packet networking systems. Prior to cPacket, he held technical leadership and management roles at Fujitsu and worked at Space R&D center developing systems for image processing and GIS applications. The application of artificial intelligence (AI) and machine learning (ML) to IT infrastructure and operations (I&O) – known as AIOps – is a hot trend in the enterprise and service provider world and for good reason: it can turbocharge IT operations.
In this article, I will take you through a brief explanation of Image Segmentation in Deep Learning. I will only explain the concept behind the image segmentation here in this article. If you want to go through the practical part of Image Segmentation you can see it's tutorial here. In Image segmentation, each pixel is classified according to the class of the object it belongs to (e.g., road, car, pedestrian, building, etc.), as shown in the figure below. Note that different objects of the same class are not distinguished.
The wearing of the face masks appears as a solution for limiting the spread of COVID-19. In this context, efficient recognition systems are expected for checking that people faces are masked in regulated areas. To perform this task, a large dataset of masked faces is necessary for training deep learning models towards detecting people wearing masks and those not wearing masks. Some large datasets of masked faces are available in the literature. However, at the moment, there are no available large dataset of masked face images that permits to check if detected masked faces are correctly worn or not.