PaperLink: a technique for hyperlinking from real paper to electronic content
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Visual Input for Pen-Based Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Visual-Based Online Signature Verification by Pen Tip Tracking
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Real-Time Retrieval for Images of Documents in Various Languages Using a Web Camera
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Capturing Digital Ink as Retrieving Fragments of Document Images
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Tracking and Retrieval of Pen Tip Positions for an Intelligent Camera Pen
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
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We propose a camera-based method for digital recovery of handwritings on ordinary paper. Our method is characterized by the following two points: (1) it requires no special device such as special paper other than a camera-pen to recover handwritings, (2) if the handwriting is on a printed document, the method is capable of localizing it onto an electronic equivalent of the printed document. The above points are enabled by the following processing. The handwriting is recovered by the LK tracking to trace the move of the pen-tip. The recovered shape is localized onto the corresponding part of the electronic document with the help of document image retrieval called LLAH (locally likely arrangement hashing). A new framework for stably estimating the homography from a camera-captured image to the corresponding electronic document allows us to localize the recovered handwritings accurately. We experimentally evaluate the accuracy, processing time and memory usage of the proposed method using 30 handwritings. From the comparison to other methods that implement alternative ways for realizing the same functionality, we have confirmed that the proposed method is superior to those other methods.