RZCR: Zero-shot Character Recognition via Radical-based Reasoning
Authors: Xiaolei Diao, Daqian Shi, Hao Tang, Qiang Shen, Yanzeng Li, Lei Wu, Hao Xu
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our method on multiple datasets, and RZCR shows promising experimental results, especially on few-sample character datasets. 5 Experiments |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Jilin University 2DISI, University of Trento 3CVL, ETH Zurich 4College of Software Engineering, Jilin University |
| Pseudocode | Yes | Algorithm 1 Weight Fusion-based Reasoning. |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. No links to repositories or explicit statements of code release are found. |
| Open Datasets | Yes | To evaluate our method on real-world character image sets which suffer from the few-sample problem, we introduce a new character image dataset called Oracle RC. We collect oracle rubbing images from [Wu, 2012] and normalize images by a denoising method [Shi et al., 2022a]. We also validate RZCR on handwritten Chinese datasets ICDAR2013 [Yin et al., 2013], HWDB1.1 [Liu et al., 2013], scene character dataset CTW [Yuan et al., 2019], and Korean dataset PE92 [KIM et al., 1996] for a comprehensive evaluation. |
| Dataset Splits | No | For all four datasets, we select 80% of the samples in each character category as the training set and the remaining as the test set. |
| Hardware Specification | No | The resolution of the input image is 416 416. We exploit data enhancement strategies, including translation, rotation, scaling, and background transformation. Parameters K=13 and M=3 are set in RIE. All experiments are conducted with Adadelta optimization where the hyperparameters are set to ρ=0.95 and ε=10 6. |
| Software Dependencies | No | All experiments are conducted with Adadelta optimization where the hyperparameters are set to ρ=0.95 and ε=10 6. |
| Experiment Setup | Yes | The resolution of the input image is 416 416. We exploit data enhancement strategies, including translation, rotation, scaling, and background transformation. Parameters K=13 and M=3 are set in RIE. All experiments are conducted with Adadelta optimization where the hyperparameters are set to ρ=0.95 and ε=10 6. |