Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation |
| Subtitle of host publication | LREC-COLING 2024 |
| Editors | Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue |
| Pages | 12679-12689 |
| Number of pages | 11 |
| ISBN (electronic) | 9782493814104 |
| Publication status | Published - 2024 |
| Event | Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024 - Hybrid, Torino, Italy Duration: 20 May 2024 → 25 May 2024 |
Abstract
Optical Character Recognition (OCR) is an established task with the objective of identifying the text present in an image. While many off-the-shelf OCR models exist, they are often trained for either scientific (e.g., formulae) or generic printed English text. Extracting text from chemistry publications requires an OCR model that is capable in both realms. Nougat, a recent tool, exhibits strong ability to parse academic documents, but is unable to parse tables in PubMed articles, which comprises a significant part of the academic community and is the focus of this work. To mitigate this gap, we present the Printed English and Chemical Equations (PEaCE) dataset, containing both synthetic and real-world records, and evaluate the efficacy of transformer-based OCR models when trained on this resource. Given that real-world records contain artifacts not present in synthetic records, we propose transformations that mimic such qualities. We perform a suite of experiments to explore the impact of patch size, multi-domain training, and our proposed transformations, ultimately finding that models with a small patch size trained on multiple domains using the proposed transformations yield the best performance. Our dataset and code is available at https://github.com/ZN1010/PEaCE.
Keywords
- Chemistry-Oriented Document Analysis, Image to Text, OCR Dataset, Optical Character Recognition (OCR)
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- Computational Theory and Mathematics
- Computer Science(all)
- Computer Science Applications
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Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation : LREC-COLING 2024. ed. / Nicoletta Calzolari; Min-Yen Kan; Veronique Hoste; Alessandro Lenci; Sakriani Sakti; Nianwen Xue. 2024. p. 12679-12689.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - PEaCE
T2 - Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024
AU - Zhang, Nan
AU - Heaton, Connor
AU - Okonsky, Sean Timothy
AU - Mitra, Prasenjit
AU - Toraman, Hilal Ezgi
N1 - Publisher Copyright: © 2024 ELRA Language Resource Association: CC BY-NC 4.0.
PY - 2024
Y1 - 2024
N2 - Optical Character Recognition (OCR) is an established task with the objective of identifying the text present in an image. While many off-the-shelf OCR models exist, they are often trained for either scientific (e.g., formulae) or generic printed English text. Extracting text from chemistry publications requires an OCR model that is capable in both realms. Nougat, a recent tool, exhibits strong ability to parse academic documents, but is unable to parse tables in PubMed articles, which comprises a significant part of the academic community and is the focus of this work. To mitigate this gap, we present the Printed English and Chemical Equations (PEaCE) dataset, containing both synthetic and real-world records, and evaluate the efficacy of transformer-based OCR models when trained on this resource. Given that real-world records contain artifacts not present in synthetic records, we propose transformations that mimic such qualities. We perform a suite of experiments to explore the impact of patch size, multi-domain training, and our proposed transformations, ultimately finding that models with a small patch size trained on multiple domains using the proposed transformations yield the best performance. Our dataset and code is available at https://github.com/ZN1010/PEaCE.
AB - Optical Character Recognition (OCR) is an established task with the objective of identifying the text present in an image. While many off-the-shelf OCR models exist, they are often trained for either scientific (e.g., formulae) or generic printed English text. Extracting text from chemistry publications requires an OCR model that is capable in both realms. Nougat, a recent tool, exhibits strong ability to parse academic documents, but is unable to parse tables in PubMed articles, which comprises a significant part of the academic community and is the focus of this work. To mitigate this gap, we present the Printed English and Chemical Equations (PEaCE) dataset, containing both synthetic and real-world records, and evaluate the efficacy of transformer-based OCR models when trained on this resource. Given that real-world records contain artifacts not present in synthetic records, we propose transformations that mimic such qualities. We perform a suite of experiments to explore the impact of patch size, multi-domain training, and our proposed transformations, ultimately finding that models with a small patch size trained on multiple domains using the proposed transformations yield the best performance. Our dataset and code is available at https://github.com/ZN1010/PEaCE.
KW - Chemistry-Oriented Document Analysis
KW - Image to Text
KW - OCR Dataset
KW - Optical Character Recognition (OCR)
UR - http://www.scopus.com/inward/record.url?scp=85195974335&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85195974335
SP - 12679
EP - 12689
BT - Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation
A2 - Calzolari, Nicoletta
A2 - Kan, Min-Yen
A2 - Hoste, Veronique
A2 - Lenci, Alessandro
A2 - Sakti, Sakriani
A2 - Xue, Nianwen
Y2 - 20 May 2024 through 25 May 2024
ER -