Journal ArticleDOI
Handwritten Chinese Text Recognition by Integrating Multiple Contexts
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TLDR
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.Abstract:
This paper presents an effective approach for the offline recognition of unconstrained handwritten Chinese texts. Under the general integrated segmentation-and-recognition framework with character oversegmentation, we investigate three important issues: candidate path evaluation, path search, and parameter estimation. For path evaluation, we combine multiple contexts (character recognition scores, geometric and linguistic contexts) from the Bayesian decision view, and convert the classifier outputs to posterior probabilities via confidence transformation. In path search, we use a refined beam search algorithm to improve the search efficiency and, meanwhile, use a candidate character augmentation strategy to improve the recognition accuracy. The combining weights of the path evaluation function are optimized by supervised learning using a Maximum Character Accuracy criterion. We evaluated the recognition performance on a Chinese handwriting database CASIA-HWDB, which contains nearly four million character samples of 7,356 classes and 5,091 pages of unconstrained handwritten texts. The experimental results show that confidence transformation and combining multiple contexts improve the text line recognition performance significantly. On a test set of 1,015 handwritten pages, the proposed approach achieved character-level accurate rate of 90.75 percent and correct rate of 91.39 percent, which are superior by far to the best results reported in the literature.read more
Citations
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Proceedings ArticleDOI
ICDAR 2015 competition on Robust Reading
Dimosthenis Karatzas,Lluis Gomez-Bigorda,Anguelos Nicolaou,Suman K. Ghosh,Andrew D. Bagdanov,Masakazu Iwamura,Jiri Matas,Lukas Neumann,Vijay Chandrasekhar,Shijian Lu,Faisal Shafait,Seiichi Uchida,Ernest Valveny +12 more
TL;DR: A new Challenge 4 on Incidental Scene Text has been added to the Challenges on Born-Digital Images, Focused Scene Images and Video Text and tasks assessing End-to-End system performance have been introduced to all Challenges.
Proceedings ArticleDOI
ICDAR 2013 Robust Reading Competition
Dimosthenis Karatzas,Faisal Shafait,Seiichi Uchida,Masakazu Iwamura,Lluís Gómez i Bigorda,Sergi Robles Mestre,Joan Mas,David Fernandez Mota,Jon Almazan,Lluís-Pere de las Heras +9 more
TL;DR: The datasets and ground truth specification are described, the performance evaluation protocols used are details, and the final results are presented along with a brief summary of the participating methods.
Proceedings ArticleDOI
ICDAR 2013 Chinese Handwriting Recognition Competition
TL;DR: This paper describes the Chinese handwriting recognition competition held at the 12th International Conference on Document Analysis and Recognition (ICDAR 2013), and reports the best results (correct rates) for classification on extracted features, offline character recognition, and online/offline handwritten text recognition.
Journal ArticleDOI
Drawing and Recognizing Chinese Characters with Recurrent Neural Network
TL;DR: Wang et al. as mentioned in this paper proposed a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generator model for drawing (generating) Chinese characters.
Journal ArticleDOI
Online and offline handwritten Chinese character recognition: A comprehensive study and new benchmark
TL;DR: In this article, a new adaptation layer is proposed to reduce the mismatch between training and test data on a particular source layer, and the adaptation process can be efficiently and effectively implemented in an unsupervised manner.
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