Proceedings ArticleDOI
A Novel Segmentation and Recognition Algorithm for Chinese Handwritten Address Character Strings
Qiang Fu,Xiaoqing Ding,Tong Liu,Yan Jiang,Zheng Ren +4 more
- Vol. 2, pp 974-977
TLDR
A dissection algorithm is applied to over-segment string image into radical series so that the correct segmentation could be achieved by merging those radicals according to the correct merging path.Abstract:
This paper presents a new method for segmenting and recognizing Chinese handwritten address character strings. First, a dissection algorithm is applied to over-segment string image into radical series so that the correct segmentation could be achieved by merging those radicals according to the correct merging path. Then, the method synthesizes layout analysis, isolated character classifier and bi-gram language model to find the best merging path and the best recognition result. The classifier used in this paper will give every isolated character image ten recognition candidates, each with corresponding recognition confidence. The parameter of bi-gram model is obtained from address database which contains more than one hundred thousand address items. In experiments on 946 mail images, the proposed method achieves correct rate of 87.2 percentread more
Citations
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Journal ArticleDOI
Handwritten Chinese Text Recognition by Integrating Multiple Contexts
TL;DR: The experimental results show that confidence transformation and combining multiple contexts improve the text line recognition performance significantly, and are superior by far to the best results reported in the literature.
Journal ArticleDOI
Handwritten Chinese/Japanese Text Recognition Using Semi-Markov Conditional Random Fields
TL;DR: A forward-backward lattice pruning algorithm is proposed to reduce the computation in training when trigram language models are used, and beam search techniques are investigated to accelerate the decoding speed.
Proceedings ArticleDOI
ICDAR 2011 Chinese Handwriting Recognition Competition
TL;DR: In the Chinese handwriting recognition competition organized with the ICDAR 2011, four tasks were evaluated: offline and online isolated character recognition, offline and offline handwritten text recognition, and to enable the training of recognition systems, the large databases CASIA-HWDB/OLHW DB were announced.
Journal ArticleDOI
A comprehensive study of hybrid neural network hidden Markov model for offline handwritten Chinese text recognition
TL;DR: An effective segmentation-free approach using a hybrid neural network hidden Markov model (NN-HMM) for offline handwritten Chinese text recognition (HCTR) and a deep convolutional neural network with automatically learned discriminative features demonstrates its superiority in the HMM framework.
Proceedings ArticleDOI
Deep neural network based hidden Markov model for offline handwritten Chinese text recognition
TL;DR: A novel segmentation-free approach using deep neural network based hidden Markov model (DNN-HMM) for offline handwritten Chinese text recognition, yielding a character error rate (CER) of 6.50%, which significantly outperforms the previously best reported oversegmentation approach.
References
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R.G. Casey,Eric Lecolinet +1 more
TL;DR: H holistic approaches that avoid segmentation by recognizing entire character strings as units are described, including methods that partition the input image into subimages, which are then classified.
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Yi Lu,Malayappan Shridhar +1 more
TL;DR: This paper presents an overview on the most important techniques used in segmenting characters from handwritten words, and summarizes the terms and measurements commonly used in handwritten character segmentation.
Journal ArticleDOI
Segmenting handwritten Chinese characters based on heuristic merging of stroke bounding boxes and dynamic programming
Lin-Yu Tseng,Rung-Ching Chen +1 more
TL;DR: A novel method which uses strokes to build stroke bounding boxes first and the knowledge-based merging operations are used to merge those stroke bounded boxes and, finally, a dynamic programming method is applied to find the best segmentation boundaries.