OpenText, a global leader in Enterprise Information Management (EIM), announced Univar Solutions EMEA, a leading distributor of chemical ingredients and services in Europe, is working with OpenText Professional Services to upgrade their deployment of OpenText Vendor Invoice Management for SAP Solutions to further transform its accounts payable operations with new AI, intelligent capture and automation capabilities. OpenText Vendor Invoice Management for SAP routes invoices automatically to the right person for resolution, approval and payment. New enhancements to the solution will boost Univar Solutions EMEA's operations by giving the company access to OCR line item recognition, improving invoice training and automating previous manual freight processing and costing. "Deep integration between OpenText and SAP is helping us continuously streamline our accounts payable processes, while continuing to find productivity gains through automation and innovation," said Brian Morgan, IT director EMEA, Univar. "We are working with OpenText Professional Services to take advantage of new capabilities in AI and process automation, ensuring that our people are focused on the customer-facing work which matters most to our business. Powerful optical character recognition combined with machine learning and intelligent automation enables content to be matched against supplier delivery notes. This helps Univar Solutions EMEA continuously identify and remove bottlenecks and automatically correct errors or inefficiencies before they impact customer satisfaction. Advanced analytics and reporting tools give Univar Solutions EMEA greater visibility over its accounts payable processes, helping ensure governance, compliance and clarity. "OpenText helps companies connect business applications, digital business processes and proprietary company content.

Optical character recognition (OCR), or the conversion of images of handwritten or printed text into machine-readable text, is a science that dates back to the early '70s. But algorithms have long struggled to make out characters that aren't parallel with horizontal planes, which is why researchers at Amazon developed what they call TextTubes. They're detectors for curved text in natural images that model said text as tubes around their medial (middle) axes. In a paper describing their work, the coauthors claim that their approach achieves state-of-the-art results on a popular OCR benchmark. As the researchers explain, scene text is typically broken down into two successive tasks: text detection and text recognition.

Does your OCR technology make sense of the data that is extracted? Traditional OCR technology provides less accuracy as it does not understand what is being extracted and hence a considerable amount of errors occur. To remove such errors it needs manual fixing which is time-consuming and will require significant resources. The AI-powered Infrrd OCR removes all such difficulties by implementing machine learning algorithms to understand the data that has been extracted and improves the output automatically. When it comes to choosing an OCR app, accuracy is one of the most important criteria.

App classification is useful in a number of applications such as adding apps to an app store or building a user model based on the installed apps. Presently there are a number of existing methods to classify apps based on a given taxonomy on the basis of their text metadata. However, text based methods for app classification may not work in all cases, such as when the text descriptions are in a different language, or missing, or inadequate to classify the app. One solution in such cases is to utilize the app images to supplement the text description. In this paper, we evaluate a number of approaches in which app images can be used to classify the apps. In one approach, we use Optical character recognition (OCR) to extract text from images, which is then used to supplement the text description of the app. In another, we use pic2vec to convert the app images into vectors, then train an SVM to classify the vectors to the correct app label. In another, we use the captionbot.ai tool to generate natural language descriptions from the app images. Finally, we use a method to detect and label objects in the app images and use a voting technique to determine the category of the app based on all the images. We compare the performance of our image-based techniques to classify a number of apps in our dataset. We use a text based SVM app classifier as our base and obtained an improved classification accuracy of 96% for some classes when app images are added.

TYSONS, VA – Appian (NASDAQ: APPN) today announced the latest version of the Appian Platform. The new release of the low-code application development platform increases the speed and business impact of low-code automation for developers, administrators, and end-users. The latest version delivers enhancements to Appian AI, further-expansion of Appian's Connected Systems architecture, integrated Health Check in every application, and simplified DevOps, making it easier than ever to develop, deploy, change, and manage Appian applications. Appian AI, a fast way to add best-of-breed artificial intelligence to any Appian application, now offers Google Cloud Translation as a Connected System. Customers can enable any app to detect languages and translate text with no coding. In addition, this release provides an updated Google Cloud Vision Connected System which now offers integration with Optical Character Recognition (OCR).

Kaspersky researchers recently found malware in an app called CamScanner, a phone-based PDF creator that includes OCR (optical character recognition) and has more than 100 million downloads in Google Play. Various resources call the app by slightly different names such as CamScanner -- Phone PDF Creator and CamScanner-Scanner to scan PDFs. Official app stores such as Google Play are usually considered a safe haven for downloading software. Unfortunately, nothing is 100% safe, and from time to time malware distributors manage to sneak their apps into Google Play. The problem is that even such a powerful company as Google can't thoroughly check millions of apps.

An increasing number of software coding tasks are being handed off to AI functions, especially in ... [ ] the low-code & no-code arenas. What software needs now is more AI. This is the universal mantra that every software application development and data platform company will beat out relentlessly throughout 2020. The rise of Artificial Intelligence (AI) and the Machine Learning (ML) processes that feed it and make it smarter has driven every software industry specialist to look for avenues where it can surgically enhance its products and services with additional layers of automated intelligence. Low-code software company Appian has clearly been drinking from the same AI Kool-Aid pot as everybody else; the company's latest platform release features a range of AI assists designed to make low code software development a more intelligently abetted process.

We propose a Text-to-Speech method to create an unseen expressive style using one utterance of expressive speech of around one second. Specifically, we enhance the disentanglement capabilities of a state-of-the-art sequence-to-sequence based system with a Variational AutoEncoder (VAE) and a Householder Flow. The proposed system provides a 22% KL-divergence reduction while jointly improving perceptual metrics over state-of-the-art. At synthesis time we use one example of expressive style as a reference input to the encoder for generating any text in the desired style. Perceptual MUSHRA evaluations show that we can create a voice with a 9% relative naturalness improvement over standard Neural Text-to-Speech, while also improving the perceived emotional intensity (59 compared to the 55 of neutral speech).

Organizations in all industries have a large number of physical documents. It can be difficult to extract text from a scanned document when it contains formats such as tables, forms, paragraphs, and check boxes. Organizations have been addressing these problems with Optical Character Recognition (OCR) technology, but it requires templates for form extraction and custom workflows. Extracting and analyzing text from images or PDFs is a classic machine learning (ML) and natural language processing (NLP) problem. When extracting the content from a document, you want to maintain the overall context and store the information in a readable and searchable format.

At BoTree Technologies, we leverage the power of AI to help companies make the most of Robotic Process Automation Technology and enhance overall efficiency. With our years of experience, we have built tools and algorithms to automate several repetitive tasks. Here are the steps through which our Robotic Process Automation tasks are executed. At BoTree, the robotic automation process begins with extracting information from structured and unstructured sources like PDFs, images, e-mails, excel sheets, and several others. This step is followed by processing the data inputs through various methods including Optical Character Recognition.