Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
MSR: Multi-Scale Shape Regression for Scene Text Detection
Authors: Chuhui Xue, Shijian Lu, Wei Zhang
IJCAI 2019 | Venue PDF | 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. |