| Autor: |
Do, Tien, Doan, Thuyen Tran, Le, Khiem, Nguyen, Thua, Le, Duy-Dinh, Ngo, Thanh Duc |
| Předmět: |
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| Zdroj: |
Vietnam Journal of Computer Science (World Scientific); Nov2024, Vol. 11 Issue 4, p569-594, 26p |
| Abstrakt: |
Key information extraction and recognition from rich text images are crucial for various applications. There are two main tasks involved in this process: Line Item Recognition (LIR) and Key Information Localization and Extraction (KILE). LIR aims at identifying and interpreting data line items in a document. The essential information in each line item is then classified or extracted, a task known as KILE. A widely used approach for this problem is sequence based, which relies on the generalization of a language model and requires a significant amount of training time. We present an effective and reliable solution to the problem by using RoBERTa, a transformer model trained on a large corpus, along with the LION optimizer to improve the training process. A comprehensive evaluation was conducted on two different benchmarks, emphasizing two different languages, English and Vietnamese. Experimental results on DocILE indicate that the proposed framework significantly improves the KILE task with a 7.24% increase in accuracy compared to the baseline and also enhances the correct recognition rate at the LIR stage. On MCOCR, the method achieved a Character Error Rate (CER) of 28.6%, which is competitive with the state-of-the-art on this dataset. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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