We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| bgshih/rctw17 | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| average_precision | Ran | bgshih/rctw17/eval_script/eval_task1.py pointer only (licence: NONE) · get_code("e8f9bd061423c2b6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Chinese is the most widely used language in the world. Algorithms that read Chinese text in natural images facilitate applications of various kinds. Despite the large potential value, datasets and competitions in the past primarily focus on English, which bares very different characteristics than Chinese. This report introduces RCTW, a new competition that focuses on Chinese text reading. The competition features a large-scale dataset with 12,263 annotated images. Two tasks, namely text localization and end-to-end recognition, are set up. The competition took place from January 20 to May 31, 2017. 23 valid submissions were received from 19 teams. This report includes dataset description, task definitions, evaluation protocols, and results summaries and analysis. Through this competition, we call for more future research on the Chinese text reading problem. The official website for the competition is http://rctw.vlrlab.net
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1708.09585")
get_code_for_paper("1708.09585")
have("1708.09585")
Connect an agent — have() is free.