Name
Abstract | ||
|---|---|---|
Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models). For example, in the case of historical ciphered manuscripts, which are usually written with invented alphabets to hide the message contents. Thus, in this paper we address this problem through a data generation technique based on Bayesian Program Learning (BPL). Contrary to traditional generation approaches, which require a huge amount of annotated images, our method is able to generate human-like handwriting using only one sample of each symbol in the alphabet. After generating symbols, we create synthetic lines to train state-of-the-art HTR architectures in a segmentation free fashion. Quantitative and qualitative analyses were carried out and confirm the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/WACV51458.2022.00262 | 2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022) |
DocType | ISSN | Citations |
Conference | 2472-6737 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Ali Souibgui | 1 | 0 | 0.34 |
| Ali Furkan Biten | 2 | 0 | 1.35 |
| Sounak Dey | 3 | 0 | 0.34 |
| Alicia Fornés | 4 | 563 | 48.56 |
| Yousri Kessentini | 5 | 0 | 0.68 |
| Lluís Gómez | 6 | 93 | 8.74 |
| Dimosthenis Karatzas | 7 | 406 | 38.13 |
| Josep Lladós | 8 | 0 | 0.34 |