Name
Abstract | ||
|---|---|---|
The Digital Age has brought with it large-scale digitization of historical records. The modern scholar of history or of other disciplines is often faced today with hundreds of thousands of readily-available and potentially-relevant full or fragmentary documents, but without computer aids that would make it possible to find the sought-after needles in the proverbial haystack of online images. The problems are even more acute when documents are handwritten, since optical character recognition does not provide quality results.We consider two tools: (1) a handwriting matching tool that is used to join together fragments of the same scribe, and (2) a paleographic classification tool that matches a given document to a large set of paleographic samples. Both tools are carefully designed not only to provide a high level of accuracy, but also to provide a clean and concise justification of the inferred results. This last requirement engenders challenges, such as sparsity of the representation, for which existing solutions are inappropriate for document analysis. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICIP.2011.6116481 | 2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | Field | DocType |
handwriting recognition,image classification,history,histograms,vectors,prototypes,dictionaries,optical character recognition,visualization | Computer vision,Digitization,Haystack,Handwriting,Computer science,Visualization,Optical character recognition,Handwriting recognition,Artificial intelligence,Contextual image classification,Palaeography | Conference |
ISSN | Citations | PageRank |
1522-4880 | 3 | 0.42 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lior Wolf | 1 | 5501 | 352.38 |
| Liza Potikha | 2 | 3 | 0.42 |
| Nachum Dershowitz | 3 | 2818 | 473.00 |
| Roni Shweka | 4 | 30 | 3.96 |
| Yaacov Choueka | 5 | 241 | 202.83 |