Primary sources: Difference between revisions
No edit summary |
No edit summary |
||
| Line 11: | Line 11: | ||
The current best results for Word Spotting using strings as queries are obtained | The current best results for Word Spotting using strings as queries are obtained | ||
using Neural Networks | using Neural Networks. However in order to obtain good results, they require a | ||
learning set and a segmentation by word, which can also be source of errors | learning set and a segmentation by word, which can also be source of errors | ||
<ref>Giotis, A. P., Sfikas, G., Gatos, B., & Nikou, C. (2017). A survey of | <ref>Giotis, A. P., Sfikas, G., Gatos, B., & Nikou, C. (2017). A survey of | ||
Revision as of 20:17, 2 November 2017
Bibliography and state of the art
When working with digitized documents, Optical Character Recognition (OCR) is traditionally used to recognized words character-by-character. However, in the case of offline handwritten text recognition, it does perform poorly. A more adapted technology to this kind of document is Word Spotting [1]. This technique does not try to directly recognize the words, but it characterizes them by their shape. Then a user can query either a word either an example (an image) and the system will return all matching occurrences.
The current best results for Word Spotting using strings as queries are obtained using Neural Networks. However in order to obtain good results, they require a learning set and a segmentation by word, which can also be source of errors [2]. Finally, new segmentation-free Word Spotting methods have appeared and seem to show good results. [3].
- ↑ Rath, T. M., & Manmatha, R. (2007). Word spotting for historical documents. International Journal on Document Analysis and Recognition, 9(2), 139-152.
- ↑ Giotis, A. P., Sfikas, G., Gatos, B., & Nikou, C. (2017). A survey of document image word spotting techniques. Pattern Recognition, 68, 310-332.
- ↑ Wilkinson, T., Lindström, J., & Brun, A. (2017). Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in Handwritten Manuscript Collections. arXiv preprint arXiv:1703.07645.