Off-Line Cursive Script Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
BrightBoard: a video-augmented environment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast HMM Algorithm for On-line Handwritten Character Recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Speeding Up On-line Recognition of Handwritten Characters by Pruning the Prototype Set
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Recognition of Cursive Roman Handwriting - Past, Present and Future
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Towards Automatic Video-based Whiteboard Reading
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
IAM-OnDB - an On-Line English Sentence Database Acquired from Handwritten Text on a Whiteboard
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Combining On-Line and Off-Line Systems for Handwriting Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
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When writing on a whiteboard, the writer stands rather than sits and the writing arm does not rest. Due to these adverse conditions when writing on a whiteboard, the script lines within the handwritten text suffer from high variations, i.e. they cannot be approximated by polynomials of low order. In this paper, we propose a novel method for identifying script lines in handwritten whiteboard notes by assigning the sample points of the script trajectory to the script lines. The optimal assignment is then found by the Viterbi algorithm. We present two ways to use the script line characterization. First, the script lines are used to normalize the skew and size of the text lines. In a second approach, the feature vector of a standard recognition system is augmented by the explicit script line membership of each sample point, aiming at reducing the confusions between characters differing in size rather than in shape (like ''s'' and ''S'' or ''e'' and ''l''). As experiments show, a relative improvement of r=3.3% in character-level and r=3.4% in word-level accuracy compared to a baseline system can be achieved with the proposed script line identification method. In addition, the written character confusion as described above can be reduced. Finally, the proposed utilizations are examined and discussed in further detail.