Aesthetic Visual Quality Evaluation of Chinese Handwritings
Authors: Rongju Sun, Zhouhui Lian, Yingmin Tang, Jianguo Xiao
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the proposed AI system provides a comparable performance with human evaluation. |
| Researcher Affiliation | Academia | Institute of Computer Science and Technology, Peking University, Beijing, P.R.China |
| Pseudocode | No | The paper describes methods in text and uses diagrams, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions that the Chinese Handwriting Aesthetic Evaluation Database (CHAED) is publicly available on their website (http://www.icst.pku.edu.cn/zlian/chin-beauty-eval/), but it does not state that the source code for the methodology is available. |
| Open Datasets | Yes | We propose a relatively large-scale Chinese Handwriting Aesthetic Evaluation Database (CHAED), which is publicly available on our website1. 1http://www.icst.pku.edu.cn/zlian/chin-beauty-eval/ |
| Dataset Splits | No | The paper states: 'For global features, half of the database is used for training and the other half for testing. To be speciļ¬c, for each character, there are 5 handwriting samples for training and 5 for testing.' It does not mention a validation split. |
| Hardware Specification | No | The paper does not explicitly describe the hardware specifications (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using back-propagation neural networks and Support Vector Machines (SVM), along with training functions TRANGDM and LEARNGDM, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We build three 4-layer back-propagation neural networks denoted as Netg, Netc and Neto for global features, component layout features and hybrid features, respectively. ... The number of neurons for Netg, Netc and Neto are respectively (22, 20, 10, 3), (10, 15, 10, 3) and (32, 40, 20, 3). We determine the structure of the 3 neural networks by adjusting the training function, adaption learning function and the number of neurons in every layer to achieve the best evaluation results in the training dataset. Here, we choose TRANGDM as the training function and LEARNGDM as the adaption learning function for these 3 networks. |