Dot Translate: Dot Translate, 1

Credits & Description:

Title of Entry: Dot Translate
Brand: Dot Translate
Product/Service: Communication Technology
Client: Dot Incorporation
Agency: SERVICEPLAN GERMANY / Munich
Agency: SERVICEPLAN KOREA / Seoul
Agency: Dot Incorporation / Seoul
Agency: SERVICEPLAN GERMANY / Munich
Serviceplan Korea / Seoul
Client / Brand: Dot Incorporation / Seoul
Design Firm: cloudandco / Seoul
Moby Digg GmbH / Munich
Standardabweichung / Munich
Production Company: Hyperinteractive GmbH / Hamburg
Production Company: Paulus Co. Ltd / Seoul
Art Director: Saurabh Kakade
Art Director: Eduardo Alvarez
Art Director: Jihyun Seo
Copywriter: Carolina Soto
Animator: Sebastian Haiss
Designer: Ki Hwan Joo
Designer: Young Woo Choi
Designer: Nara Ok
Digital Artist: Handong Ryu
Director: Dahoon Chung
Producer: Kyungsin Kim
Creative Director: Yeong Kyu Yoo
Creative Director: Franz Röppischer
Creative Director: Lorenz Langgartner
Creative Director: Maximilian Heitsch
Chief Creative Officer: Alex Schill
Chief Digital Officer: Jae Seong Joo
Chief Marketing Officer: Eric Ju Yoon Kim
Chief Marketing Officer: Ahrum Choi
Chief Marketing Officer: Julie Kang
Chief Technology Officer: Ki Sung
Creative Technologist: Dieter Pries
Data Scientist: Vasil Vasilev
Director of Photography: Yongsang Cho
User Interface Designer: Marco Kawan
Creative: Carolina Soto
Creative: Bozhidar Yovchev
Creative: Anton Petrov
Creative: Daniel Kuhnlein
Creative: Niklas May
Creative: Yoon Seo
Date of Release: 2019-03-15
Notes: For 285 million blind and visually impaired people worldwide, only 3% of all text content is available in braille.
Dot Translate is the first AI braille translator working at near-human accuracy. Trained with millions of highly accurate translation samples, made accessible through an intuitive web interface, Dot devices and other digital braille devices. Formats and sources cover a wide range: type, e-pub, pdf, txt, doc, rtf, html, voice and even translating whole websites. Existing braille translation software works by applying a small set of pre-defined rules, which doesn’t cover many cases. Because of the complex nature of existing translation systems and high cost we set out to make a more accessible, accurate translation. By using a machine learning based system, we were able to provide a wide range of formats more fitting to human context instead of the ambiguous content previously available. And because usage is key to improving accuracy, we made the software free to use for both braille readers and non-readers around the world.