Codesota · Computer Vision · Optical Character Recognition · i2l-140kTasks/Computer Vision/Optical Character Recognition
Optical Character Recognition · benchmark dataset · 2020 · EN

i2l-140k.

Dataset from Papers With Code

Submit a result ↵
§ 01 · Leaderboard

Best published scores.

2 results indexed across 1 metric. Shaded row marks current SOTA; ties broken by submission date.

Primary
accuracy · higher is better
bleu
2 rows
#ModelOrgSubmittedPaper / codebleu
01I2L-NOPOOL—Feb 2018Teaching Machines to Code: Neural Markup Generation with… · code89.09
02I2L-STRIPS—Feb 2018Teaching Machines to Code: Neural Markup Generation with… · code89
Fig 2 · Rows sorted by score within each metric. Shaded row marks SOTA. Dates reflect model or paper release where available, otherwise the date Codesota accessed the source.
§ 04 · Literature

1 paper
tied to this benchmark.

Every paper below corresponds to at least one row in the leaderboard above. Click through for the arXiv preprint and, when available, the reference implementation.

§ 06 · Contribute

Have a score that beats
this table?

Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.

Submit a result ↵Read submission guide
What a submission needs
  • 01A public checkpoint or API endpoint
  • 02A reproduction script with frozen commit + seed
  • 03Declared evaluation environment (Python, deps)
  • 04One row per metric declared by this dataset
  • 05A contact so we can follow up on discrepancies