Heng Zhang
Orcid: 0000-0001-9448-4031
According to our database1,
Heng Zhang
authored at least 13 papers
between 2010 and 2024.
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Bibliography
2024
An approach for handwritten Chinese text recognition unifying character segmentation and recognition.
Pattern Recognit., 2024
Proceedings of the Document Analysis and Recognition - ICDAR 2024 - 18th International Conference, Athens, Greece, August 30, 2024
Adaptive Scaling and Refined Pyramid Feature Fusion Network for Scene Text Segmentation.
Proceedings of the Document Analysis and Recognition - ICDAR 2024 - 18th International Conference, Athens, Greece, August 30, 2024
2022
An Efficient Prototype-Based Model for Handwritten Text Recognition with Multi-loss Fusion.
Proceedings of the Frontiers in Handwriting Recognition - 18th International Conference, 2022
2021
Regularizing CTC in Expectation-Maximization Framework with Application to Handwritten Text Recognition.
Proceedings of the International Joint Conference on Neural Networks, 2021
2020
Table detection and cell segmentation in online handwritten documents with graph attention networks.
Proceedings of the MMAsia 2020: ACM Multimedia Asia, 2020
2019
Oracle Character Recognition by Nearest Neighbor Classification with Deep Metric Learning.
Proceedings of the 2019 International Conference on Document Analysis and Recognition, 2019
2017
Keyword spotting in handwritten chinese documents using semi-markov conditional random fields.
Eng. Appl. Artif. Intell., 2017
2014
Character confidence based on N-best list for keyword spotting in online Chinese handwritten documents.
Pattern Recognit., 2014
2013
Keyword Spotting from Online Chinese Handwritten Documents using One-versus-All Character Classification Model.
Int. J. Pattern Recognit. Artif. Intell., 2013
2012
A confidence-based method for keyword spotting in online Chinese handwritten documents.
Proceedings of the 21st International Conference on Pattern Recognition, 2012
Proceedings of the Pattern Recognition - Chinese Conference, 2012
2010
Keyword Spotting from Online Chinese Handwritten Documents Using One-vs-All Trained Character Classifier.
Proceedings of the International Conference on Frontiers in Handwriting Recognition, 2010