revolutionize access to handwritten documents
The ScanTent is the optimal solution for document scanning on the go with low cost and high quality. In combination with the DocScan app it enables you to hold a document with both hands and to scan it with your smart phone without pressing any button. DocScan takes the picture once a page is turned, and stops as long as the same page is presented to the camera.
The specialized mount on top of the ScanTent enables you to place the smart phone in a convenient way and thus guarantees an optimal viewing angle and a constant distance. White LED strips provide a uniform lighting that maximize image quality. Disassembling the ScanTent and its low weight allow for compact transportation.
Currently experiments are going on with a small set of prototypes. Once we have finished this phase we plan to market the ScanTent in 2018. If you want to express your interest in the ScanTent please write a short email to: email@example.com
empowering the ScanTent
DocScan is an Android app designed for the ScanTent. It detects pages in the live preview and makes high quality scans. An automatic series mode takes an image once a page is turned. It therefore enables you to scan books or documents quickly without interacting with your mobile. Please report issues concerning DocScan here.
QR Code Generator
DocScan is capable of reading QR codes, which encode special information about documents - such as document title. By simply scanning a QR code with DocScan, documents can be automatically created. If you want to use this feature, fill out the forms in the following and print out the page (a printer friendly version of the page is automatically generated).
We have tested eight different smartphones and measured their resolution if mounted to the scantent. You can see their field of view and resolution in the illustration below. DocScan does not support the Nokia 7 Plus Tele and iPhone 7.
Field of view and resolution of different smartphones
The ScanTent is developed as part of the READ project by members of the Computer Vision Lab, TU Wien and Universität Innsbruck. It has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 674943. If you are interested, write us: firstname.lastname@example.org