Segmentation-free word spotting in historical Bangla handwritten document using Wave Kernel Signature
Abstract In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the do...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Das, Sugata [verfasserIn] Mandal, Sekhar [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Pattern Analysis & Applications - Springer-Verlag, 1999, 23(2019), 2 vom: 30. Apr., Seite 593-610 |
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| Übergeordnetes Werk: |
volume:23 ; year:2019 ; number:2 ; day:30 ; month:04 ; pages:593-610 |
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| DOI / URN: |
10.1007/s10044-019-00823-1 |
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| 520 | |a Abstract In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the document page. A window is placed centered at each keypoint to obtain the WKS descriptors. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. We use an efficient search technique which calculates minimum energy difference between query word and document image to spot where the query word appears in the document image. The proposed method is tested on three historical Bangla handwritten datasets, one Bangla handwritten dataset, one old Bangla-printed dataset and one historical English handwritten dataset. To substantiate the goodness of the proposed method, its performance is measured using standard metrics. | ||
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10.1007/s10044-019-00823-1 doi (DE-627)SPR039494349 (SPR)s10044-019-00823-1-e DE-627 ger DE-627 rakwb eng Das, Sugata verfasserin aut Segmentation-free word spotting in historical Bangla handwritten document using Wave Kernel Signature 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the document page. A window is placed centered at each keypoint to obtain the WKS descriptors. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. We use an efficient search technique which calculates minimum energy difference between query word and document image to spot where the query word appears in the document image. The proposed method is tested on three historical Bangla handwritten datasets, one Bangla handwritten dataset, one old Bangla-printed dataset and one historical English handwritten dataset. To substantiate the goodness of the proposed method, its performance is measured using standard metrics. Document image (dpeaa)DE-He213 Word spotting (dpeaa)DE-He213 Segmentation-free (dpeaa)DE-He213 Wave Kernel Signature (WKS) (dpeaa)DE-He213 SIFT keypoint detector (dpeaa)DE-He213 Mandal, Sekhar verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 23(2019), 2 vom: 30. Apr., Seite 593-610 (DE-627)SPR008209189 nnns volume:23 year:2019 number:2 day:30 month:04 pages:593-610 https://dx.doi.org/10.1007/s10044-019-00823-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 2 30 04 593-610 |
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10.1007/s10044-019-00823-1 doi (DE-627)SPR039494349 (SPR)s10044-019-00823-1-e DE-627 ger DE-627 rakwb eng Das, Sugata verfasserin aut Segmentation-free word spotting in historical Bangla handwritten document using Wave Kernel Signature 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the document page. A window is placed centered at each keypoint to obtain the WKS descriptors. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. We use an efficient search technique which calculates minimum energy difference between query word and document image to spot where the query word appears in the document image. The proposed method is tested on three historical Bangla handwritten datasets, one Bangla handwritten dataset, one old Bangla-printed dataset and one historical English handwritten dataset. To substantiate the goodness of the proposed method, its performance is measured using standard metrics. Document image (dpeaa)DE-He213 Word spotting (dpeaa)DE-He213 Segmentation-free (dpeaa)DE-He213 Wave Kernel Signature (WKS) (dpeaa)DE-He213 SIFT keypoint detector (dpeaa)DE-He213 Mandal, Sekhar verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 23(2019), 2 vom: 30. Apr., Seite 593-610 (DE-627)SPR008209189 nnns volume:23 year:2019 number:2 day:30 month:04 pages:593-610 https://dx.doi.org/10.1007/s10044-019-00823-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 2 30 04 593-610 |
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10.1007/s10044-019-00823-1 doi (DE-627)SPR039494349 (SPR)s10044-019-00823-1-e DE-627 ger DE-627 rakwb eng Das, Sugata verfasserin aut Segmentation-free word spotting in historical Bangla handwritten document using Wave Kernel Signature 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the document page. A window is placed centered at each keypoint to obtain the WKS descriptors. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. We use an efficient search technique which calculates minimum energy difference between query word and document image to spot where the query word appears in the document image. The proposed method is tested on three historical Bangla handwritten datasets, one Bangla handwritten dataset, one old Bangla-printed dataset and one historical English handwritten dataset. To substantiate the goodness of the proposed method, its performance is measured using standard metrics. Document image (dpeaa)DE-He213 Word spotting (dpeaa)DE-He213 Segmentation-free (dpeaa)DE-He213 Wave Kernel Signature (WKS) (dpeaa)DE-He213 SIFT keypoint detector (dpeaa)DE-He213 Mandal, Sekhar verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 23(2019), 2 vom: 30. Apr., Seite 593-610 (DE-627)SPR008209189 nnns volume:23 year:2019 number:2 day:30 month:04 pages:593-610 https://dx.doi.org/10.1007/s10044-019-00823-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 2 30 04 593-610 |
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10.1007/s10044-019-00823-1 doi (DE-627)SPR039494349 (SPR)s10044-019-00823-1-e DE-627 ger DE-627 rakwb eng Das, Sugata verfasserin aut Segmentation-free word spotting in historical Bangla handwritten document using Wave Kernel Signature 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the document page. A window is placed centered at each keypoint to obtain the WKS descriptors. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. We use an efficient search technique which calculates minimum energy difference between query word and document image to spot where the query word appears in the document image. The proposed method is tested on three historical Bangla handwritten datasets, one Bangla handwritten dataset, one old Bangla-printed dataset and one historical English handwritten dataset. To substantiate the goodness of the proposed method, its performance is measured using standard metrics. Document image (dpeaa)DE-He213 Word spotting (dpeaa)DE-He213 Segmentation-free (dpeaa)DE-He213 Wave Kernel Signature (WKS) (dpeaa)DE-He213 SIFT keypoint detector (dpeaa)DE-He213 Mandal, Sekhar verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 23(2019), 2 vom: 30. Apr., Seite 593-610 (DE-627)SPR008209189 nnns volume:23 year:2019 number:2 day:30 month:04 pages:593-610 https://dx.doi.org/10.1007/s10044-019-00823-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 2 30 04 593-610 |
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10.1007/s10044-019-00823-1 doi (DE-627)SPR039494349 (SPR)s10044-019-00823-1-e DE-627 ger DE-627 rakwb eng Das, Sugata verfasserin aut Segmentation-free word spotting in historical Bangla handwritten document using Wave Kernel Signature 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the document page. A window is placed centered at each keypoint to obtain the WKS descriptors. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. We use an efficient search technique which calculates minimum energy difference between query word and document image to spot where the query word appears in the document image. The proposed method is tested on three historical Bangla handwritten datasets, one Bangla handwritten dataset, one old Bangla-printed dataset and one historical English handwritten dataset. To substantiate the goodness of the proposed method, its performance is measured using standard metrics. Document image (dpeaa)DE-He213 Word spotting (dpeaa)DE-He213 Segmentation-free (dpeaa)DE-He213 Wave Kernel Signature (WKS) (dpeaa)DE-He213 SIFT keypoint detector (dpeaa)DE-He213 Mandal, Sekhar verfasserin aut Enthalten in Pattern Analysis & Applications Springer-Verlag, 1999 23(2019), 2 vom: 30. Apr., Seite 593-610 (DE-627)SPR008209189 nnns volume:23 year:2019 number:2 day:30 month:04 pages:593-610 https://dx.doi.org/10.1007/s10044-019-00823-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 2 30 04 593-610 |
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Abstract In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the document page. A window is placed centered at each keypoint to obtain the WKS descriptors. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. We use an efficient search technique which calculates minimum energy difference between query word and document image to spot where the query word appears in the document image. The proposed method is tested on three historical Bangla handwritten datasets, one Bangla handwritten dataset, one old Bangla-printed dataset and one historical English handwritten dataset. To substantiate the goodness of the proposed method, its performance is measured using standard metrics. |
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Abstract In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the document page. A window is placed centered at each keypoint to obtain the WKS descriptors. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. We use an efficient search technique which calculates minimum energy difference between query word and document image to spot where the query word appears in the document image. The proposed method is tested on three historical Bangla handwritten datasets, one Bangla handwritten dataset, one old Bangla-printed dataset and one historical English handwritten dataset. To substantiate the goodness of the proposed method, its performance is measured using standard metrics. |
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Abstract In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the document page. A window is placed centered at each keypoint to obtain the WKS descriptors. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. We use an efficient search technique which calculates minimum energy difference between query word and document image to spot where the query word appears in the document image. The proposed method is tested on three historical Bangla handwritten datasets, one Bangla handwritten dataset, one old Bangla-printed dataset and one historical English handwritten dataset. To substantiate the goodness of the proposed method, its performance is measured using standard metrics. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR039494349</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201126004713.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10044-019-00823-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR039494349</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10044-019-00823-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Das, Sugata</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Segmentation-free word spotting in historical Bangla handwritten document using Wave Kernel Signature</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the document page. A window is placed centered at each keypoint to obtain the WKS descriptors. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. We use an efficient search technique which calculates minimum energy difference between query word and document image to spot where the query word appears in the document image. The proposed method is tested on three historical Bangla handwritten datasets, one Bangla handwritten dataset, one old Bangla-printed dataset and one historical English handwritten dataset. To substantiate the goodness of the proposed method, its performance is measured using standard metrics.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Document image</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Word spotting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Segmentation-free</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wave Kernel Signature (WKS)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SIFT keypoint detector</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mandal, Sekhar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Pattern Analysis & Applications</subfield><subfield code="d">Springer-Verlag, 1999</subfield><subfield code="g">23(2019), 2 vom: 30. Apr., Seite 593-610</subfield><subfield code="w">(DE-627)SPR008209189</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2</subfield><subfield code="g">day:30</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:593-610</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10044-019-00823-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2019</subfield><subfield code="e">2</subfield><subfield code="b">30</subfield><subfield code="c">04</subfield><subfield code="h">593-610</subfield></datafield></record></collection>
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