Kiyoharu Aizawa, Yusuke Matsui, Jeonghun Baek
We lifted 2 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| clovaai/deep-text-recognition-benchmark | canonical | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| checkImageIsValid | Not yet run | clovaai/deep-text-recognition-benchmark/create_lmdb_dataset.py code served (permissive licence) · get_code("c7b92e1633b0d2da") |
| tensor2im | Not yet run | clovaai/deep-text-recognition-benchmark/dataset.py code served (permissive licence) · get_code("b78e81e855133e45") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recognizing irregular texts has been a challenging topic in text recognition. To encourage research on this topic, we provide a novel comic onomatopoeia dataset (COO), which consists of onomatopoeia texts in Japanese comics. COO has many arbitrary texts, such as extremely curved, partially shrunk texts, or arbitrarily placed texts. Furthermore, some texts are separated into several parts. Each part is a truncated text and is not meaningful by itself. These parts should be linked to represent the intended meaning. Thus, we propose a novel task that predicts the link between truncated texts. We conduct three tasks to detect the onomatopoeia region and capture its intended meaning: text detection, text recognition, and link prediction. Through extensive experiments, we analyze the characteristics of the COO. Our data and code are available at https://github.com/ku21fan/COO-Comic-Onomatopoeia.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2207.04675")
get_code_for_paper("2207.04675")
have("2207.04675")
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