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.

Yeelen Knegtering, CEO & Co-founder of Klippa, is passionate about developing digital products that help people to save time on administrative hassle and spend time on the things they love. With a degree in Information Technology at the University of Groningen, he started Klippa with the idea that there had to be a better way to organize and manage receipts. Now, Klippa is a document digitization company with a focus on digitizing and automating document streams for companies.

The great amount of information that can be stored in electronic media is growing up daily. Many of them is got mainly by typing, such as the huge of information obtained from web 2.0 sites; or scaned and processing by an Optical Character Recognition software, like the texts of libraries and goverment offices. Both processes introduce error in texts, so it is difficult to use the data for other purposes than just to read it, i.e. the processing of those texts by other applications like e-learning, learning of languages, electronic tutorials, data minning, information retrieval and even more specialized systems such as tiflologic software, specifically blinded people-oriented applications like automatic reading, where the text would be error free as possible in order to make easier the text to speech task, and so on. In this paper it is showed an application of automatic recognition and correction of ortographic errors in electronic texts. This task is composed of three stages: a) error detection; b) candidate corrections generation; and c) correction -selection of the best candidate. The proposal is based in part of speech text categorization, word similarity, word diccionaries, statistical measures, morphologic analisys and n-grams based language model of Spanish.

Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents. Think about, for example, the ZIP codes on letters at the post office and the automation needed to recognize these five digits. Perfect recognition of these codes is necessary in order to sort mail automatically and efficiently. Included among the other applications that may come to mind is OCR (Optical Character Recognition) software. OCR software must read handwritten text, or pages of printed books, for general electronic documents in which each character is well defined.

Machine Learning, DeepLearning and artificial intelligence algorithms, in general, are attracting increasing attention in various industrial and social fields. However, many interesting algorithms were developed a few years ago.

It uses Amazon Textract, a machine learning service that automatically extracts text, handwriting, and data from scanned documents to provide highly …