Name
Abstract | ||
|---|---|---|
Transcribing documents from the printing press era, a challenge in its own right, is more complicated when documents interleave multiple languages—a common feature of 16th century texts. Additionally, many of these documents precede consistent orthographic conventions, making the task even harder. We extend the state-of-the-art historical OCR model of Berg-Kirkpatrick et al. (2013) to handle word-level code-switching between multiple languages. Further, we enable our system to handle spelling variability, including now-obsolete shorthand systems used by printers. Our results show average relative character error reductions of 14% across a variety of historical texts. |
Year | Venue | Field |
|---|---|---|
2015 | HLT-NAACL | Transcription (linguistics),Printing press,Computer science,Code-switching,Spelling,Natural language processing,Artificial intelligence,Linguistics,Machine learning,Historical document |
DocType | Citations | PageRank |
Conference | 3 | 0.41 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan Garrette | 1 | 207 | 11.18 |
| Hannah Alpert-Abrams | 2 | 3 | 0.41 |
| Taylor Berg-Kirkpatrick | 3 | 554 | 35.93 |
| Dan Klein | 4 | 8083 | 495.21 |