Key Information Extraction and Recognition from Rich Text Images
| Autor: | Tien Do, Thuyen Tran Doan, Khiem Le, Thua Nguyen, Duy-Dinh Le, Thanh Duc Ngo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Vietnam Journal of Computer Science, Vol 11, Iss 04, Pp 569-594 (2024) |
| Druh dokumentu: | article |
| ISSN: | 21968888 2196-8896 2196-8888 |
| DOI: | 10.1142/S2196888824500131 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|