MSR: Multi-Scale Shape Regression for Scene Text Detection
Authors: Chuhui Xue, Shijian Lu, Wei Zhang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments over several public datasets show that the proposed MSR obtains superior detection performance for both curved and straight text lines of different lengths and orientations. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanyang Technological University 2School of Control Science and Engineering, Shandong University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Synth Text [Gupta et al., 2016] contains more than 800,000 synthetic scene text images most of which are at word level with multi-oriented rectangular annotations. CTW1500 [Yuliang et al., 2017] consists of 1,000 training images and 500 test images... Total-Text [Ch ng and Chan, 2017] has 1,255 training images and 300 test images... MSRA-TD500 [Yao et al., 2012] consists of 300 training images and 200 test images. ICDAR2015 [Karatzas et al., 2015] has 1000 training images and 500 test images... |
| Dataset Splits | Yes | CTW1500 [Yuliang et al., 2017] consists of 1,000 training images and 500 test images... Total-Text [Ch ng and Chan, 2017] has 1,255 training images and 300 test images... MSRA-TD500 [Yao et al., 2012] consists of 300 training images and 200 test images. ICDAR2015 [Karatzas et al., 2015] has 1000 training images and 500 test images... |
| Hardware Specification | Yes | The proposed technique is implemented using Tensorflow on a regular GPU workstation with 2 Nvidia Geforce GTX 1080 Ti. |
| Software Dependencies | No | The paper mentions "Tensorflow" and "Adam optimizer" and "Res Net-50" but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The network is optimized by Adam optimizer [Kingma and Ba, 2014] with a starting learning rate of 10 4. ... The network is pre-trained on the Synth Text, which is then fine-tuned by using the training images of each evaluated dataset with a batch size of 10. ... Parameters λ is the weight to balance the two losses which is empirically set at 1.0 in our implemented system. |