The ICDAR 2019 cTDaR is to evaluate the performance of methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done.
仓库
Dod-o 的仓库
(0 stars) (0 forks) (0 个已索引 issue) (0 个开放 good first issue)
Dod-o/LeetCodePython
(28 stars) (10 forks) (0 个已索引 issue) (0 个开放 good first issue)
Dod-o/MineContextPython
MineContext is your proactive context-aware AI partner(Context-Engineering+ChatGPT Pulse)
(1 star) (1 fork) (0 个已索引 issue) (0 个开放 good first issue)
力求囊括主流NLP模型练手项目,不断更新中
(295 stars) (65 forks) (0 个已索引 issue) (0 个开放 good first issue)
手写实现李航《统计学习方法》书中全部算法
(10,550 stars) (2,838 forks) (0 个已索引 issue) (0 个开放 good first issue)
(23 stars) (6 forks) (0 个已索引 issue) (0 个开放 good first issue)
Dod-o/fairseqPython
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
(0 stars) (0 forks) (0 个已索引 issue) (0 个开放 good first issue)
Flow function examples for flows.network
(0 stars) (0 forks) (0 个已索引 issue) (0 个开放 good first issue)
(0 stars) (0 forks) (0 个已索引 issue) (0 个开放 good first issue)
(1 star) (0 forks) (0 个已索引 issue) (0 个开放 good first issue)
(0 stars) (2 forks) (0 个已索引 issue) (0 个开放 good first issue)
Dod-o/unilmPython
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
(0 stars) (0 forks) (0 个已索引 issue) (0 个开放 good first issue)
MCP for xiaohongshu.com
(0 stars) (0 forks) (0 个已索引 issue) (0 个开放 good first issue)