TY - GEN
T1 - Building Handwriting Recognizers by Leveraging Skeletons of Both Offline and Online Samples
AU - Zhang, Xiong
AU - Wang, Min
AU - Wang, Lijuan
AU - Huo, Qiang
AU - Li, Haifeng
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/20
Y1 - 2015/11/20
N2 - We present an approach to leveraging both offline and online handwriting samples to build a single recognizer for recognizing both offline and online handwritings. Given a training set of offline handwriting samples and another set of online handwriting samples, a skeleton is derived first from each offline handwriting sample via vectorization. Then both the skeleton samples and online handwriting samples are normalized and rendered by using the same method to generate a combined training set of skeleton images. Finally a handwriting recognizer based on Deep Bidirectional Long Short-Term Memory (DBLSTM) and Hidden Markov Model (HMM) is built from the skeleton images. In recognition, a preprocessing step consistent with that in training is applied to an unknown offline or online handwriting sample to derive a skeleton image, which is recognized by the hybrid DBLSTM-HMM handwriting recognition system accordingly. We have built such a recognizer by using IAM benchmark databases of offline and online English handwritings plus an internal online handwriting corpus, which outperforms the recognizers built from either offline or online handwriting samples only.
AB - We present an approach to leveraging both offline and online handwriting samples to build a single recognizer for recognizing both offline and online handwritings. Given a training set of offline handwriting samples and another set of online handwriting samples, a skeleton is derived first from each offline handwriting sample via vectorization. Then both the skeleton samples and online handwriting samples are normalized and rendered by using the same method to generate a combined training set of skeleton images. Finally a handwriting recognizer based on Deep Bidirectional Long Short-Term Memory (DBLSTM) and Hidden Markov Model (HMM) is built from the skeleton images. In recognition, a preprocessing step consistent with that in training is applied to an unknown offline or online handwriting sample to derive a skeleton image, which is recognized by the hybrid DBLSTM-HMM handwriting recognition system accordingly. We have built such a recognizer by using IAM benchmark databases of offline and online English handwritings plus an internal online handwriting corpus, which outperforms the recognizers built from either offline or online handwriting samples only.
UR - https://www.scopus.com/pages/publications/84962550555
U2 - 10.1109/ICDAR.2015.7333793
DO - 10.1109/ICDAR.2015.7333793
M3 - 会议稿件
AN - SCOPUS:84962550555
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 406
EP - 410
BT - 13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings
PB - IEEE Computer Society
T2 - 13th International Conference on Document Analysis and Recognition, ICDAR 2015
Y2 - 23 August 2015 through 26 August 2015
ER -