Renters at the Mar Vista Gardens public housing complex were skeptical as Roderick Strong laid out the plan: Instead of getting mail delivered to their doors, residents would pick it up at new, centralized spots around the 43-acre community in Del Rey, a Los Angeles neighborhood west of Culver City. Strong, the Culver City Post Office postmaster, called the new system "the launch of a new era." He said the shift was being considered to ensure the safety of mail carriers who had been menaced by dog bites and other threats. Few tenants seemed swayed, however, as they listened by phone and the web during a remote meeting on a recent weekday. Daisy Vega, president of the resident advisory council, asked Strong why such a change was being planned for their housing complex and not for "the other side of town."

Recent progress in deep learning has led to the development of Optical Character Recognition (OCR) systems which perform remarkably well. Most research has been around recurrent networks as well as complex gated layers which make the overall solution complex and difficult to scale. In this paper, we present an Efficient And Scalable TExt Recognizer (EASTER) to perform optical character recognition on both machine printed and handwritten text. Our model utilises 1-D convolutional layers without any recurrence which enables parallel training with considerably less volume of data. We experimented with multiple variations of our architecture and one of the smallest variant (depth and number of parameter wise) performs comparably to RNN based complex choices. Our 20-layered deepest variant outperforms RNN architectures with a good margin on benchmarking datasets like IIIT-5k and SVT. We also showcase improvements over the current best results on offline handwritten text recognition task. We also present data generation pipelines with augmentation setup to generate synthetic datasets for both handwritten and machine printed text.

The United States Postal Service today suspended measures that caused mail-delivery delays across the country in recent weeks, including an initiative designed to remove hundreds of mail-sorting machines from active rotation. There are no public plans to reinstate machines that have already been taken offline, but starting today, no additional units will be removed from service until after the US presidential election in November. Vice reported last week that the USPS had begun retiring mail-sorting machines across the country "without any official explanation or reason given," significantly slowing employees' ability to organize and send mail. A total of 671 machines, or 10 percent of the postal service's stock, were scheduled to be taken offline, according to The Washington Post. This was part of a larger initiative to "strengthen the Postal Service" by Postmaster General Louis DeJoy, who joined the USPS in June after 35 years as an executive at a large supply-chain logistics company.

Postmaster General DeJoy to testify before Senate; American Postal Workers Union president Mark Dimondstein weighs in. "The new Postmaster General has instituted a number of policies that are truly slowing down mail," Mark Dimondstein, president of the American Postal Workers Union, told "America's Newsroom" on Tuesday. Dimondstein made the comment in anticipation of Postmaster General Louis DeJoy testifying on Friday about the U.S. Postal Service amid the battle over mail-in ballots before the Senate Homeland Security and Governmental Affairs Committee. The hearing comes after congressional Democrats over the weekend demanded DeJoy and the chairman of the U.S. Postal Service Board of Governors Robert Duncan testify over recent "sweeping and dangerous operational changes" at the agency that they claim are "slowing" the mail and "jeopardizing the integrity of the 2020 election. A source familiar with the plans told Fox News that DeJoy has agreed to appear on Monday. Host Trace Gallagher asked Dimondstein on Tuesday if he believes, as Democrats contest, that DeJoy "is trying to sabotage the election by making cuts that slow down the flow of mail?" "I can't really judge the motivation," he said in response. "We just have to look at the deeds and the deeds thus far is the new Postmaster General has instituted a number of policies that are truly slowing down mail.

The twenty-first century is considered as an era of technology. We have entered the digital world, and it has become our way of life. The novel pandemic has further increased the demand for technology. Artificial Intelligence (AI), also referred to as machine intelligence, has been drastically disrupting the world of technology. It is developing at a fast pace and will soon become an inevitable part of our lives.

The United States Postal Service is reportedly retiring mail sorting machines. According to Postmaster general Louis DeJoy, the agency is in a "dire" financial situation, citing significant falls in mail volume, "a broken business model" and an inadequate management strategy as reasons for the "impending liquidity crisis." Dejoy has been implementing changes since assuming the role in June, including organizational restructuring and a management hiring freeze, saying they're meant to "strengthen the Postal Service." But there appear to be moves that he might have left out. Vice is reporting that the USPS is also retiring mail sorting machines around the country "without any official explanation or reason given."

Retrieving information from documents and forms has long been a challenge, and even now at the time of writing, organisations are still handling significant amounts of paper forms that need to be scanned, classified and mined for specific information to enable downstream automation and efficiencies. Automating this extraction and applying intelligence is in fact a fundamental step toward digital transformation that organisations are still struggling to solve in an efficient and scalable manner. An example could be a bank that receives hundreds of kilograms of very diverse remittance forms a day that need to be processed manually by people in order to extract a few key fields. Or medicinal prescriptions need to be automated to extract the prescribed medication and quantity. Typically organisations will have built text mining and search solutions which are often tailored for a scenario, with baked in application logic, resulting in an often brittle solution that is difficult and expensive to maintain.

With evolving technologies, intelligent automation has become a top priority for many executives in 2020. Forrester predicts the industry will continue to grow from $250 million in 2016 to $12 billion in 2023. With more companies identifying and implementation the Artificial Intelligence (AI) and Machine Learning (ML), there is seen a gradual reshaping of the enterprise. Industries across the globe integrate AI and ML with businesses to enable swift changes to key processes like marketing, customer relationships and management, product development, production and distribution, quality check, order fulfilment, resource management, and much more. AI includes a wide range of technologies such as machine learning, deep learning (DL), optical character recognition (OCR), natural language processing (NLP), voice recognition, and so on, which creates intelligent automation for organizations across multiple industrial domains when combined with robotics.

This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.

Machine vision is the classification and tracking of real-world objects based on visual, x-ray, laser, or other signals. Optical character recognition was an early success of machine vision, but deciphering handwritten text remains a work in progress. The quality of machine vision depends on human labeling of a large quantity of reference images. The simplest way for machines to start learning is through access to this labeled data. Within the next five years, video-based computer vision will be able to recognize actions and predict motion--for example, in surveillance systems.