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.

For those of us who worry that Facebook may have serious boundary issues when it comes to the personal information of its users, Mark Zuckerberg's recent comments at Harvard should get the heart racing. Zuckerberg dropped by the university last month ostensibly as part of a year of conversations with experts about the role of technology in society, "the opportunities, the challenges, the hopes, and the anxieties." His nearly two-hour interview with Harvard law school professor Jonathan Zittrain in front of Facebook cameras and a classroom of students centered on the company's unprecedented position as a town square for perhaps 2 billion people. To hear the young CEO tell it, Facebook was taking shots from all sides--either it was indifferent to the ethnic hatred festering on its platforms or it was a heavy-handed censor deciding whether an idea was allowed to be expressed. Zuckerberg confessed that he hadn't sought out such an awesome responsibility.

For those of us who worry that Facebook may have serious boundary issues when it comes to the personal information of its users, Mark Zuckerberg's recent comments at Harvard should get the heart racing. Zuckerberg ostensibly dropped by the university last month as part of a year of conversations with experts about the role of technology in society, "the opportunities, the challenges, the hopes, and the anxieties." His nearly two-hour interview with the Harvard law school professor Jonathan Zittrain in front of Facebook cameras and a classroom of students centered on the company's unprecedented position as a town square for perhaps two billion people. To hear the young CEO tell it, Facebook was taking shots from all sides--either it was indifferent to the ethnic hatred festering on its platforms or it was a heavy-handed censor deciding whether an idea was allowed to be expressed. Zuckerberg confessed that he hadn't sought out such an awesome responsibility.

AI Foundry is aiming to further cut the time it takes to originate a mortgage by adding, among other things, artificial intelligence technology designed to improve on optical character recognition. The fintech firm on Tuesday launched a new "cognitive business automation platform" aimed at improving automated decision-making related to loans, and a new version of its Agile Mortgages technology, which loan officers use to automate the collection and organization of documents. The new technology classifies data and information that at a 90%-plus accuracy rate is "significantly more accurate than optical character recognition," according to a company press release. "Competition in the mortgage lending industry is intense, and while many companies have deployed point-of-sale solutions for the customer, the back-end processes have not undergone a digital transformation, until now," Steve Butler, founder and general manager of AI Foundry, said in a press release. How does your digital mortgage stack up? "We will truly disrupt the'status quo' by automating the mortgage application process and enabling lenders to complement those front-end capabilities with one-day mortgage approvals. This not only opens enormous potential for acquiring and delighting new customers; it also drives down the cost per mortgage, so lenders can be more profitable."