MRZ is an abbreviation for Machine Readable Zone, whereas MRTD refers to Machine Readable Travel Document. MRZ is a format found on most passports and identity documents worldwide. It contains the document holder's data in a form which is both visually readable and encoded with optical character recognition. Built on the latest advances in machine learning, BlinkID enables scanning of MRZ with unsurpassed accuracy and speed.

Jumio, the AI-powered trusted identity as a service provider, announced gains in the speed and accuracy of its verification services, as well as a more intuitive user experience. These gains come after a two year period during which Jumio invested heavily automation enabled through a variety of machine learning, artificial intelligence, and optical character recognition (OCR). As a result of its investment in supervised machine learning models, Jumio was also able to recently launch Jumio Go, its new identity verification solution powered exclusively by AI. The investment has also improved Jumio's current suite of identity verification and authentication services. "We're seeing across-the-board improvements in our ability to automate virtually every phase of the identity verification process, making our core solutions even faster, easier and more accurate for our customers and their end users," said Labhesh Patel, Jumio CTO and chief scientist. Jumio is highlighting a number of improvements that have come about as a result of its focus over the past two years.

JOURNAL OF XXX CLASS FILES, VOL. 1, NO. 1, JUNE 2019 1 Air-Writing Translater: A Novel Unsupervised Domain Adaptation Method for Inertia-Trajectory Translation of In-air Handwriting Songbin Xu, Y ang Xue, Xin Zhang, Lianwen Jin As a new way of human-computer interaction, inertial sensor based in-air handwriting can provide a natural and unconstrained interaction to express more complex and richer information in 3D space. However, most of the existing in-air handwriting work is mainly focused on handwritten character recognition, which makes these work suffer from poor readability of inertial signal and lack of labeled samples. T o address these two problems, we use unsupervised domain adaptation method to reconstruct the trajectory of inertial signal and generate inertial samples using online handwritten trajectories. In this paper, we propose an Air-Writing Translater model to learn the bidirectional translation between trajectory domain and inertial domain in the absence of paired inertial and trajectory samples. Through semantic-level adversarial training and latent classification loss, the proposed model learns to extract domain-invariant content between inertial signal and trajectory, while preserving semantic consistency during the translation across the two domains. We carefully design the architecture, so that the proposed framework can accept inputs of arbitrary length and translate between different sampling rates. We also conduct experiments on two public datasets: 6DMG (in-air handwriting dataset) and CT (handwritten trajectory dataset), the results on the two datasets demonstrate that the proposed network successes in both Inertia-to Trajectory and Trajectory-to-Inertia translation tasks. I NTRODUCTION I NAIR handwriting refers to a novel way of human-computer interaction (HCI), which freely writes meaningful characters in 3D space and then converts them into user-to-computer commands. Compared with general motion gestures, in-air handwriting is more complicated and provides more abundant expressions. As modern MEMS(Micro-Electro- Mechanical System) inertial sensors become smaller and more energy efficient, they have been universally employed in portable and wearable devices such as smartphones and wristbands. Unlike optical devices, inertial sensors do not suffer from illumination interference and obstruction. Therefore, inertial sensor based in-air handwriting has widely attracted researchers' attention [1]-[4]. Most of the existing work is mainly focused on in-air handwriting recognition (IAHR) [5]-[8]. But in the research of IAHR, there are usually two problems. Firstly, the inertial signal is full of abstractness and lack of readability, because it is a series of temporal sequences representing motion shifting, as illustrated in Fig.1(a).

First things first: what is TTS? TTS or Text-to-Speech technology converts text into spoken speech. If you know Siri or those handy voice GPS directions on smartphones, then congratulations! Since 1000 AD, humans have strived to create synthetic speech, but it didn't enter the mainstream until the mid 1970s – early 1980s when computer operating systems began implementing it. Walt Tetschner, leader of the group that produced DECtalk in 1983, explains that while the voice wasn't perfect, it was still natural sounding and was used by companies such as MCI and Mtel (two-way paging).

Every end user is a customer, and the quality of the customer journey is everything, regardless of whether the objective is purchasing a product or service or engaging in content fruition. End users can be website visitors, application, device, service, and machine users, online learners or teachers, and more. Text to speech allows content owners to respond to the different needs and desires of each user in terms of how they interact with the content.

Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the 8th century. Over 3 millions books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts.

Deloitte US has deployed a technology-enabled solution -- CognitiveTax Insight (CogTax) -- to provide a more efficient analysis of clients' indirect tax data set. CogTax has the ability to analyse the full population, if desired, of clients' accounts payable transactions compared to a traditional, sampled approach. The solution can help companies to proactively avoid overpaying their indirect tax liabilities. CogTax leverages optical character recognition (OCR), along with advanced machine learning algorithms and analytics, to analyse a full population of data and documents to assist clients with indirect tax overpayment recovery and reduce the potential for future over or underpayments. "CogTax moves tax analysis from a manual, administrative process to an automated, machine learning process that brings with it accuracy and scale previously not achievable," said Deval Reddy, indirect tax principal in the multistate tax services practice, Deloitte Tax LLP.

Machine learning has played a very important role in the development of technology that has a large impact on our everyday lives. However, machine learning is also influencing the direction of technology that is not as commonplace. Text to speech technology is a prime example. Text to speech technology predates machine learning by over a century. However, machine learning has made the technology more reliable than ever.

Amazon Textract is a machine learning service that makes it easy to extract text and data from virtually any document. Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables. This allows you to use Amazon Textract to instantly "read" virtually any type of document and accurately extract text and data without the need for any manual effort or custom code. The blog post Automatically extract text and structured data from documents with Amazon Textract shows how to use Amazon Textract to automatically extract text and data from scanned documents without any machine learning (ML) experience. One of the use cases covered in the post is search and discovery.

Large and powerful Typhoon Hagibis is disrupting mail and parcel delivery services in Japan while forcing amusement facilities to suspend operations despite a three-day weekend through Monday, a public holiday. On Saturday, Japan Post Co. halted mail collection and delivery operations, and over-the-counter services at post offices mainly in the Kanto region including Tokyo due to suspensions of public transport services and other factors. With delays having already occurred in some mail and parcel delivery services, Japan Post is expecting the disruptions to spread throughout the country. Yamato Transport Co. halted or shortened the day's parcel pickup and delivery operations in many areas to ensure safety of workers. Delays in parcel deliveries are expected to continue until around Monday due in part to road clolsures, company officials said.