The benefits of digitizing a company's documentation are numerous, and converting papers to PDF files for archiving has been a common practice for years. Still, Artificial Intelligence's new horizons are giving archives a new lease of life, converting them into data sources. Fiscal records, contracts, communications, invoices, and any other documents related to the type of business are filed in every firm. Document digitization is widely used to make physical spaces more efficient and allow consultation based on criteria entered manually during the digital preservation of paper supports. Even though a process has existed for years, digital development may offer new value and innovation.

Today we are accessible to a mountain of intelligent technologies. And no doubt that computer vision stores a vital space among all of them. When we talk about computer vision, the foremost application that we think of is Image Recognition. But indeed, a computer vision also encompasses OCR (Optical Character Recognition) algorithm, which allows seamless computer operations. In this article, we will discuss the origin, advancements, OCR tasks, and OCR industry applications that are enriching the OCR Pipeline.

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilised efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organisations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory.

If your business is driven by data, Optical Character Recognition (OCR) -- as most of us know it -- is not the answer. For those of you who view OCR as an industry staple for document processing, let me explain. OCR as a technology has been around for ages and it still has its place in processing unstructured document formats like PDFs, images, and other text formats that cannot be edited digitally. Users can quickly convert those files into editable documents. In short, it's a terrific technology for enabling you to edit and search for files that may have been "frozen."

By 2022, robotics trends and forecasts will improve the global technology industry. The pandemic presented both challenges and opportunities for logistics and supermarket robotics companies. Unexpected supply chain pressures and product shortages have highlighted the need to improve supply chain efficiency. Various industries have also suffered from labor shortages caused by health and safety regulations. The lessons learned in 2021 can be applied to the goals and trends of the robotics industry in 2022.

This article surveys the landscape of semiconductor materials and devices research for the acceleration of machine learning (ML) algorithms. We observe a disconnect between the semiconductor and device physics and engineering communities, and the digital logic and computer hardware architecture communities. The article first provides an overview of the principles of computational complexity and fundamental physical limits to computing and their relation to physical systems. The article then provides an introduction to ML by presenting three key components of ML systems: representation, evaluation, and optimisation. The article then discusses and provides examples of the application of emerging technologies from the demiconductor and device physics domains as solutions to computational problems, alongside a brief overview of emerging devices for computing applications. The article then reviews the landscape of ML accelerators, comparing fixed-function and reprogrammable digital logic with novel devices such as memristors, resistive memories, magnetic memories, and probabilistic bits. We observe broadly lower performance of ML accelerators based on novel devices and materials when compared to those based on digital complimentary metal-oxide semiconductor (CMOS) technology, particularly in the MNIST optical character recognition task, a common ML benchmark, and also highlight the lack of a trend of progress in approaches based on novel materials and devices. Lastly, the article proposes figures of merit for meaningful evaluation and comparison of different ML implementations in the hope of fostering a dialogue between the materials science, device physics, digital logic, and computer architecture communities by providing a common frame of reference for their work.

OCR software have been critical to businesses looking to grow quickly by leveraging digital workflows & automated processes. OCR software automate data capture from scanned documents/images and digitize the data in convenient, editable formats that fit into organizational workflows. Scanning & processing documents such as invoices, receipts, and images for valuable data has traditionally been a manual process fraught with errors and delays. OCR software solutions help businesses save time and resources that would otherwise be spent on data entry & manual validation/verification. Modern OCR software are fast, accurate and can handle common document processing constraints such as poorly formatted scans, handwritten documents, low quality images/scans, and blemishes that would have traditionally required extended manual interventions. More and more organizations are automating document processing workflows to go paperless and leverage cloud-based digital solutions that improve bottom lines.

For years the finance industry, which encompasses organizations in financial services, insurance, and banking, has been a strong adopter of artificial intelligence (AI). Financial organizations are using artificial intelligence in multiple ways, including to improve service, better understand customers, gauge risk and predict market movements, and speed claims processing. For example, chatbots and natural language processing (NLP) assist with customer support, Optical Character Recognition (OCR) helps with the ingestion of information from documents, computer vision analyzes images and videos to speed claim processing, and machine learning models assess risk, detect fraud, and help determine rating and pricing. Results from GlobalData's 2021 ICT Customer Insight survey reveal that between 25-27% of digital spending by companies in finance will go towards artificial intelligence and machine learning. Interestingly, GlobalData's survey indicated that the portion of budget allocated to disruptive technologies is slightly higher for small financial organizations than for the largest businesses, as show in Figure 2.

Yann Andre LeCun, a French computer scientist who focuses on machine learning, computer vision, mobile robotics, and computational neuroscience, recently tweeted that one of his articles has been rejected from NeurIPS 2021. Yann LeCun is a Silver Professor at New York University's Courant Institute of Mathematical Sciences and Vice President, Chief AI Scientist at Facebook. He is well-known for his work on optical character recognition and computer vision using convolutional neural networks (CNNs) and is often regarded as the inventor of convolutional nets. He is also a co-creator of the DjVu image compression technology. The author is a multifaceted individual with academic and industrial experience in artificial intelligence, machine learning, deep learning, computer vision, intelligent data analysis, data mining, data compression, digital library systems, and robotics.

This code pattern is part of the Getting started with IBM Maximo Visual Inspection learning path. In this code pattern, learn how to use optical character recognition (OCR) and the IBM Maximo Visual Inspection object recognition service to identify and read license plates. Using IBM Maximo Visual Inspection and the Custom Inference Scripts, you can build an object detection model to identify license plates from images of cars. The models in the IBM Maximo Visual Inspection object recognition service can identify portions of images that represent a license plate. Then, the post custom inference script can crop this area and use open source to perform OCR on the text to return the license plate.