Employees and contractors at the Centers of Medicare and Medicaid Services spend countless hours every year reviewing thousands of medical records to ensure the accuracy of Medicare Advantage payments. An automated intake tool is working to change that. Using emerging technologies such as robotic process automation, optical character recognition, machine learning and artificial intelligence, KPMG's Intake Process Automation Tool ingests records as they are submitted and identifies potential problems according to set parameters, submission rules and coding guidance. Specifically, RPA orchestrates the steps of the intake process, OCR digitizes the scanned document and then AI and machine learning are applied to understand the document and extract the information necessary to validate the information. Intake PA stands to save CMS time and money, said Payam Mousavi, KPMG's lead director for intelligent automation for governments and the technical lead for the CMS project.

Adam Bertram is a 20-year veteran of IT. Adam focuses on DevOps, system management, and automation technologies as well as various cloud platforms. He is a Microsoft Cloud and Datacenter Management MVP and efficiency nerd that enjoys teaching others a better way to leverage automation.

Need to extract content from a document quickly and automatically? Amazon today announced the general availability of Textract, a cloud-hosted and fully managed service that uses machine learning to parse data tables, forms, and whole pages for text and data. Virginia), US West (Oregon), and EU (Ireland) regions and will expand to additional regions in the coming year. Textract is more capable than your average optical character recognition system. From files stored in an Amazon S3 bucket, it's able to suss out the contents of fields and tables and the context in which this information is presented, like names and social security numbers in tax forms or totals from photographed receipts.

In this paper, we present a new dataset for Form Understanding in Noisy Scanned Documents (FUNSD). Form Understanding (FoUn) aims at extracting and structuring the textual content of forms. The dataset comprises 200 fully annotated real scanned forms. The documents are noisy and exhibit large variabilities in their representation making FoUn a challenging task. The proposed dataset can be used for various tasks including text detection, optical character recognition (OCR), spatial layout analysis and entity labeling/linking. To the best of our knowledge this is the first publicly available dataset with comprehensive annotations addressing the FoUn task. We also present a set of baselines and introduce metrics to evaluate performance on the FUNSD dataset. The FUNSD dataset can be downloaded at https://guillaumejaume.github. io/FUNSD/.

Abstract: In this work, we propose a non-autoregressive seq2seq model that converts text to spectrogram. It is fully convolutional and obtains about 17.5 times speed-up over Deep Voice 3 at synthesis while maintaining comparable speech quality using a WaveNet vocoder. Interestingly, it has even fewer attention errors than the autoregressive model on the challenging test sentences. Furthermore, we build the first fully parallel neural text-to- speech system by applying the inverse autoregressive flow (IAF) as the parallel neural vocoder. Our system can synthesize speech from text through a single feed-forward pass. We also explore a novel approach to train the IAF from scratch as a generative model for raw waveform, which avoids the need for distillation from a separately trained WaveNet.

Natural disasters aren't just threats to people and buildings, they can also erase history -- by destroying rare archival documents. As a safeguard, scholars in Japan are digitizing the country's centuries-old paper records, typically by taking a scan or photo of each page. But while this method preserves the content in digital form, it doesn't mean researchers will be able to read it. Millions of physical books and documents were written in an obsolete script called Kuzushiji, legible to fewer than 10 percent of Japanese humanities professors. "We end up with billions of images which will take researchers hundreds of years to look through," said Tarin Clanuwat, researcher at Japan's ROIS-DS Center for Open Data in the Humanities.

The benefits this provides to finance, for instance, are plentiful. With RPA, you can automate a range of important finance tasks you'd do in SAP, such as invoice entry and collection, validation, reconciliation, payroll, and expense management. Even if your purchase orders or invoices are coming in via fax, mail, or emailed PDFs, computer vision and optical character recognition (OCR) can enter that data automatically, so employees don't have to type it manually.

On Thursday, Attorney General William Barr released a redacted version of the report produced by special counsel Robert Mueller. And strikethroughs look particularly bad!) You can also download the document to search it at your leisure. Disclaimer: This version of the Mueller report was processed using the Recognize Text feature of Adobe Acrobat Pro Version 2019.010.20099 The PDF was then exported into Microsoft Word.

AI and machine learning are breathing new life and business opportunities into that tired old phrase, "automating paper-based processes." I saw an example of how software developers can inject intelligence into business processes in this VIDEO interview at SAP TechEd with Dr. Matthias Sessler, technical enablement lead, SAP Leonardo Machine Learning Foundation. The demo showed image object detection combined with scene text recognition and optical character recognition (OCR), three of the 23 ready-to-use services on offer from the SAP Leonardo Machine Learning Foundation. Image object detection automatically identifies objects from images like a bus, and often tag teams with scene text recognition, which reads the fine print, such as the station name and line number. "With smartphones, it's so much easier for people to take a picture of something. However, developers need a toolbox so they can quickly build machine learning capabilities to make sense of these images and text," said Sessler.

AI and machine learning are breathing new life and business opportunities into that tired old phrase, "automating paper-based processes." I saw an example of how software developers can inject intelligence into business processes in this VIDEO interview at SAP TechEd with Dr. Matthias Sessler, technical enablement lead, SAP Leonardo Machine Learning Foundation. The demo showed image object detection combined with scene text recognition and optical character recognition (OCR), three of the 23 ready-to-use services on offer from the SAP Leonardo Machine Learning Foundation. Image object detection automatically identifies objects from images like a bus, and often tag teams with scene text recognition, which reads the fine print, such as the station name and line number. "With smartphones, it's so much easier for people to take a picture of something. However, developers need a toolbox so they can quickly build machine learning capabilities to make sense of these images and text," said Sessler.