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== Bibliography and state of the art == | == Bibliography and state of the art == | ||
When working with digitized documents, Optical Character Recognition (OCR) is | When working with digitized documents, Optical Character Recognition (OCR) is traditionally used to recognize words in a character-by-character fashion. However, in the case of offline handwritten text recognition, it does perform rather poorly. A more suited approach to this problem is the Word Spotting technology. First proposed in S. Madhvanath et al., (1996) <ref name="Manmatha1997"> | ||
traditionally used to recognize words in a character-by-character fashion. However, in the case of offline handwritten text recognition, it does perform rather poorly. A more suited approach to this | |||
Manmatha, R., Han, C., & Riseman, E. M. (1996, June). Word spotting: A new | Manmatha, R., Han, C., & Riseman, E. M. (1996, June). Word spotting: A new | ||
approach to indexing handwriting. In Computer Vision and Pattern Recognition, | approach to indexing handwriting. In Computer Vision and Pattern Recognition, | ||
1996. Proceedings CVPR'96, 1996 IEEE Computer Society Conference on (pp. | 1996. Proceedings CVPR'96, 1996 IEEE Computer Society Conference on (pp. | ||
631-637). IEEE. </ref>, this approach does not | 631-637). IEEE. </ref>, this approach does not attempt to recognize characters or wors per say, | ||
but | but tries instead to retrieve all instances matching a user query (either a word or an | ||
image) in a set of document images. | image) in a set of document images. It is noted that Word Spotting is of primary interest in the case of historical documents. <ref>Rath, T. M., & | ||
Manmatha, R. (2007). Word spotting for historical documents. International | Manmatha, R. (2007). Word spotting for historical documents. International | ||
Journal on Document Analysis and Recognition, 9(2), 139-152.</ref> <ref name="Giotis2017">Giotis, A. P., Sfikas, G., Gatos, B., & Nikou, C. (2017). A survey of | Journal on Document Analysis and Recognition, 9(2), 139-152.</ref> <ref name="Giotis2017">Giotis, A. P., Sfikas, G., Gatos, B., & Nikou, C. (2017). A survey of | ||
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There are three main characteristics that constrain Word Spotting methods<ref name="Giotis2017"/>: | There are three main characteristics that constrain Word Spotting methods<ref name="Giotis2017"/>: | ||
# The need for segmentation, which can be by word, | # The need for a segmentation, which can be done by word, line or arbitrarily. Bad segmentation might results in errors if poorly chosen, thus there have been attempts at coming up with segmentation-free methods cf. Leydier et Al. (2007)<ref> Leydier, Y., Lebourgeois, F., & Emptoz, H. (2007). Text search for medieval manuscript images. Pattern Recognition, 40(12), 3552-3567.</ref>. | ||
# The way of searching a word, either by string | # The way of searching for a word, either by string or by example. Search by example is more restrictive, provided an example as first to be identified prior to querying, while other methods have attempted to allow for a search by string cf. Edwards et Al. (2005) <ref> Edwards, J., Teh, Y. W., Bock, R., Maire, M., Vesom, G., & Forsyth, D. A. (2005). Making latin manuscripts searchable using gHMM's. In Advances in Neural Information Processing Systems (pp. 385-392).</ref> | ||
# | # The use of a training set, i.e. human annotated data. Most often, methods based on a training set appear to perform far better <ref name="Giotis2017"/>. | ||
Currently, the best performing methods | |||
However, | Currently, the best performing methods rely on segmentation by word, querying by example and using a training set. Moreover, they also make use of Neural Networks.<ref name="Giotis2017"/> | ||
However, a preferable method would not need segmentation, and allow to query by string. New methods with such features have emerged and show promising results, c.f. Wilkinson et al. (2017)<ref>Wilkinson, T., Lindström, J., & | |||
Brun, A. (2017). Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting | Brun, A. (2017). Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting | ||
in Handwritten Manuscript Collections. arXiv preprint arXiv:1703.07645.</ref>. | in Handwritten Manuscript Collections. arXiv preprint arXiv:1703.07645.</ref>. | ||
Revision as of 01:56, 10 November 2017
Bibliography and state of the art
When working with digitized documents, Optical Character Recognition (OCR) is traditionally used to recognize words in a character-by-character fashion. However, in the case of offline handwritten text recognition, it does perform rather poorly. A more suited approach to this problem is the Word Spotting technology. First proposed in S. Madhvanath et al., (1996) [1], this approach does not attempt to recognize characters or wors per say, but tries instead to retrieve all instances matching a user query (either a word or an image) in a set of document images. It is noted that Word Spotting is of primary interest in the case of historical documents. [2] [3]
There are three main characteristics that constrain Word Spotting methods[3]:
- The need for a segmentation, which can be done by word, line or arbitrarily. Bad segmentation might results in errors if poorly chosen, thus there have been attempts at coming up with segmentation-free methods cf. Leydier et Al. (2007)[4].
- The way of searching for a word, either by string or by example. Search by example is more restrictive, provided an example as first to be identified prior to querying, while other methods have attempted to allow for a search by string cf. Edwards et Al. (2005) [5]
- The use of a training set, i.e. human annotated data. Most often, methods based on a training set appear to perform far better [3].
Currently, the best performing methods rely on segmentation by word, querying by example and using a training set. Moreover, they also make use of Neural Networks.[3] However, a preferable method would not need segmentation, and allow to query by string. New methods with such features have emerged and show promising results, c.f. Wilkinson et al. (2017)[6].
- ↑ Manmatha, R., Han, C., & Riseman, E. M. (1996, June). Word spotting: A new approach to indexing handwriting. In Computer Vision and Pattern Recognition, 1996. Proceedings CVPR'96, 1996 IEEE Computer Society Conference on (pp. 631-637). IEEE.
- ↑ Rath, T. M., & Manmatha, R. (2007). Word spotting for historical documents. International Journal on Document Analysis and Recognition, 9(2), 139-152.
- ↑ 3.0 3.1 3.2 3.3 Giotis, A. P., Sfikas, G., Gatos, B., & Nikou, C. (2017). A survey of document image word spotting techniques. Pattern Recognition, 68, 310-332.
- ↑ Leydier, Y., Lebourgeois, F., & Emptoz, H. (2007). Text search for medieval manuscript images. Pattern Recognition, 40(12), 3552-3567.
- ↑ Edwards, J., Teh, Y. W., Bock, R., Maire, M., Vesom, G., & Forsyth, D. A. (2005). Making latin manuscripts searchable using gHMM's. In Advances in Neural Information Processing Systems (pp. 385-392).
- ↑ 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.