Name
Abstract | ||
|---|---|---|
The indexing and searching of historical documents have garnered attention in recent years due to massive digitization efforts of important collections worldwide. Pure textual search in these corpora is a problem since optical character recognition (OCR) is infamous for performing poorly on such historical material, which often suffer from poor preservation. We propose a novel text-based method for searching through noisy text. Our system represents words as vectors, projects queries and candidates obtained from the OCR into a common space, and ranks the candidates using a metric suited to nearest-neighbor search. We demonstrate the practicality of our method on typewritten German documents from the WWII era. |
Year | Venue | DocType |
|---|---|---|
2020 | DH | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Badamdorj Taivanbat | 1 | 0 | 0.34 |
| Adiel Ben-Shalom | 2 | 6 | 1.98 |
| Nachum Dershowitz | 3 | 2818 | 473.00 |
| Lior Wolf | 4 | 5501 | 352.38 |