We lifted 13 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 |
|---|---|---|
| harvardnlp/im2markup | canonical | 0 of 1 |
| da03/Attention-OCR | reimplementation | 1 of 1 |
| jvpoulos/Attention-OCR | pwc_unofficial | 0 of 8 |
| CT2001/image-to-latex | pwc_unofficial | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| process_args | Ran | da03/Attention-OCR/src/launcher.py code served (permissive licence) · get_code("5f977334a9330cac") |
| FMM_func | Not yet run | CT2001/image-to-latex/data-preprocess/no_chinese.py code served (permissive licence) · get_code("5bfcddf02c8fcf58") |
| find_first | Not yet run | CT2001/image-to-latex/image_to_latex/models/resnet_transformer.py code served (permissive licence) · get_code("d3fb41b62eaedad7") |
| generate_square_subsequent_mask | Not yet run | CT2001/image-to-latex/image_to_latex/models/resnet_transformer.py code served (permissive licence) · get_code("9fae5025ebafa98e") |
| get_wmt_enfr_dev_set | Not yet run | jvpoulos/Attention-OCR/src/model/data_utils.py code served (permissive licence) · get_code("ec532e8f70289f38") |
| get_wmt_enfr_train_set | Not yet run | jvpoulos/Attention-OCR/src/model/data_utils.py code served (permissive licence) · get_code("0a7b6ca2e2bf4659") |
| max_2x1pool | Not yet run | jvpoulos/Attention-OCR/src/model/cnn.py code served (permissive licence) · get_code("3ca4a31edc44c233") |
| max_2x2pool | Not yet run | jvpoulos/Attention-OCR/src/model/cnn.py code served (permissive licence) · get_code("32711cdafa63eda4") |
| maybe_download | Not yet run | jvpoulos/Attention-OCR/src/model/data_utils.py code served (permissive licence) · get_code("43ea785c6d00f3e4") |
| process_args | Not yet run | harvardnlp/im2markup/scripts/preprocessing/preprocess_images.py code served (permissive licence) · get_code("ae08cb323bcbd595") |
| process_args | Not yet run | jvpoulos/Attention-OCR/src/launcher.py code served (permissive licence) · get_code("e1ee189649199b8f") |
| rnn_decoder | Not yet run | jvpoulos/Attention-OCR/src/model/seq2seq.py code served (permissive licence) · get_code("f44847e8b1c4bd91") |
| var_random | Not yet run | jvpoulos/Attention-OCR/src/model/cnn.py code served (permissive licence) · get_code("213cd44559181629") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a neural encoder-decoder model to convert images into presentational markup based on a scalable coarse-to-fine attention mechanism. Our method is evaluated in the context of image-to-LaTeX generation, and we introduce a new dataset of real-world rendered mathematical expressions paired with LaTeX markup. We show that unlike neural OCR techniques using CTC-based models, attention-based approaches can tackle this non-standard OCR task. Our approach outperforms classical mathematical OCR systems by a large margin on in-domain rendered data, and, with pretraining, also performs well on out-of-domain handwritten data. To reduce the inference complexity associated with the attention-based approaches, we introduce a new coarse-to-fine attention layer that selects a support region before applying attention.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1609.04938")
get_code_for_paper("1609.04938")
have("1609.04938")
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