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17th ICDAR 2023: San José, CA, USA - Part IV
- Gernot A. Fink, Rajiv Jain, Koichi Kise, Richard Zanibbi:
Document Analysis and Recognition - ICDAR 2023 - 17th International Conference, San José, CA, USA, August 21-26, 2023, Proceedings, Part IV. Lecture Notes in Computer Science 14190, Springer 2023, ISBN 978-3-031-41684-2
Posters: Handwriting
- Yan-Rong Wang, Da-Han Wang, Xiao-Long Yun, Yan-Ming Zhang, Fei Yin, Shunzhi Zhu:
A Shallow Graph Neural Network with Innovative Node Updating for Online Handwritten Stroke Classification. 3-19 - Haisong Ding, Bozhi Luan, Dongnan Gui, Kai Chen, Qiang Huo:
Improving Handwritten OCR with Training Samples Generated by Glyph Conditional Denoising Diffusion Probabilistic Model. 20-37 - Yi Chen
, Heng Zhang, Cheng-Lin Liu:
Improved Learning for Online Handwritten Chinese Text Recognition with Convolutional Prototype Network. 38-53 - Brian Kenji Iwana
Vision Conformer: Incorporating Convolutions into Vision Transformer Layers. 54-69 - Zhigang Li, Li Liu, Taorong Qiu, Yue Lu, Ching Y. Suen:
Modeling Cross-layer Interaction for Chinese Calligraphy Style Classification. 70-84 - Oliver Tüselmann
Exploring Semantic Word Representations for Recognition-Free NLP on Handwritten Document Images. 85-100 - Peter Garst, R. Reeve Ingle, Yasuhisa Fujii:
OCR Language Models with Custom Vocabularies. 101-115 - Arooba Maqsood, Nauman Riaz, Adnan Ul-Hasan, Faisal Shafait:
A Unified Architecture for Urdu Printed and Handwritten Text Recognition. 116-130 - Andrei Afonin, Andrii Maksai, Aleksandr Timofeev, Claudiu Musat:
Sampling and Ranking for Digital Ink Generation on a Tight Computational Budget. 131-146 - Samuel Londner, Yoav Phillips, Hadar Miller, Nachum Dershowitz, Tsvi Kuflik, Moshe Lavee:
Linguistic Knowledge Within Handwritten Text Recognition Models: A Real-World Case Study. 147-164 - Jing Li, Bin Dong, Qiu-Feng Wang, Lei Ding, Rui Zhang, Kaizhu Huang:
Decoupled Learning for Long-Tailed Oracle Character Recognition. 165-181 - Denis Coquenet
Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-End Handwritten Document Recognition. 182-199 - Giorgos Sfikas, George Retsinas, Panagiotis Dimitrakopoulos, Basilis Gatos, Christophoros Nikou:
Shared-Operation Hypercomplex Networks for Handwritten Text Recognition. 200-216 - Aleksandr Timofeev, Anastasiia Fadeeva, Andrei Afonin, Claudiu Musat, Andrii Maksai:
DSS: Synthesizing Long Digital Ink Using Data Augmentation, Style Encoding and Split Generation. 217-235 - Simon Corbillé
Precise Segmentation for Children Handwriting Analysis by Combining Multiple Deep Models with Online Knowledge. 236-252 - Daniel Parres
Fine-Tuning Vision Encoder-Decoder Transformers for Handwriting Text Recognition on Historical Documents. 253-268 - Jan Kohút
Fine-Tuning is a Surprisingly Effective Domain Adaptation Baseline in Handwriting Recognition. 269-286 - Masayuki Honda, Hung Tuan Nguyen, Cuong Tuan Nguyen, Kha Cong Nguyen, Ryosuke Odate, Takashi Kanemaru, Masaki Nakagawa:
Incremental Teacher Model with Mixed Augmentations and Scheduled Pseudo-label Loss for Handwritten Text Recognition. 287-301 - Heng Wang
AFFGANwriting: A Handwriting Image Generation Method Based on Multi-feature Fusion. 302-312 - Niharika Vadlamudi
SeamFormer: High Precision Text Line Segmentation for Handwritten Documents. 313-331 - Jiarong Huang, Dezhi Peng, Hongliang Li, Hao Ni, Lianwen Jin:
SegCTC: Offline Handwritten Chinese Text Recognition via Better Fusion Between Explicit and Implicit Segmentation. 332-349 - Maham Jahangir, Muhammad Imran Malik, Faisal Shafait:
Adversarial Attacks on Convolutional Siamese Signature Verification Networks. 350-365 - Panagiotis Kaddas, Konstantinos Palaiologos, Basilis Gatos, Vassilis Katsouros, Katerina Christopoulou:
A System for Processing and Recognition of Greek Byzantine and Post-Byzantine Documents. 366-376 - Jan Kohút
Towards Writing Style Adaptation in Handwriting Recognition. 377-394 - Maroua Mehri
Historical Document Image Segmentation Combining Deep Learning and Gabor Features. 395-410 - Xinzhe Jiang
Group, Contrast and Recognize: A Self-supervised Method for Chinese Character Recognition. 411-427 - Zeeshan Memon, Adnan Ul-Hasan, Faisal Shafait:
Content-Aware Urdu Handwriting Generation. 428-444 - Sujoy Paul, Gagan Madan, Akankshya Mishra, Narayan Hegde, Pradeep Kumar, Gaurav Aggarwal:
Weakly Supervised Information Extraction from Inscrutable Handwritten Document Images. 445-463
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