Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition

Abstract When a PDF417 barcode are recognized, there are major recognition processes such as segmentation, normalization, and decoding. Among them, the segmentation and normalization steps are very important because they have a strong influence on the rate of barcode recognition. There are also prev...
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Gespeichert in:

Autor*in:	Kim, Young Jung [verfasserIn] Lee, Jong Yun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	PDF417 Barcode recognition Morphology Hough transform Perspective transform

Übergeordnetes Werk:	Enthalten in: Wireless personal communications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 89(2016), 3 vom: 14. Jan., Seite 893-911
Übergeordnetes Werk:	volume:89 ; year:2016 ; number:3 ; day:14 ; month:01 ; pages:893-911

Links:	Volltext

DOI / URN:	10.1007/s11277-016-3171-6

Katalog-ID:	SPR018574955

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520			\|a Abstract When a PDF417 barcode are recognized, there are major recognition processes such as segmentation, normalization, and decoding. Among them, the segmentation and normalization steps are very important because they have a strong influence on the rate of barcode recognition. There are also previous segmentation and normalization techniques of processing barcode image, but some issues as follows. First, the previous normalization techniques need an additional restoration process and apply an interpolation process. Second, the previous recognition algorithms recognize a barcode image well only when it is placed in the predefined rectangular area. Therefore, we propose a novel segmentation and normalization method in PDF417 with the aims of improving its recognition rate and precision. The segmentation process to detect the barcode area in an image uses the conventional morphology and Hough transform methods. The normalization process of the bar code region is based on the conventional perspective transformation and warping algorithms. In addition, we perform experiments using both experimental and actual data for evaluating our algorithms. Consequently, our experimental results can be summarized as follows. First, our method showed a stable performance over existing PDF417 barcode detection and recognition. Second, it overcame the limitation problem where the location of an input image should locate in a predefined rectangle area. Finally, it is expected that our result can be used as a restoration tool of printed images such as documents and pictures.
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allfields	10.1007/s11277-016-3171-6 doi (DE-627)SPR018574955 (SPR)s11277-016-3171-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Kim, Young Jung verfasserin aut Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract When a PDF417 barcode are recognized, there are major recognition processes such as segmentation, normalization, and decoding. Among them, the segmentation and normalization steps are very important because they have a strong influence on the rate of barcode recognition. There are also previous segmentation and normalization techniques of processing barcode image, but some issues as follows. First, the previous normalization techniques need an additional restoration process and apply an interpolation process. Second, the previous recognition algorithms recognize a barcode image well only when it is placed in the predefined rectangular area. Therefore, we propose a novel segmentation and normalization method in PDF417 with the aims of improving its recognition rate and precision. The segmentation process to detect the barcode area in an image uses the conventional morphology and Hough transform methods. The normalization process of the bar code region is based on the conventional perspective transformation and warping algorithms. In addition, we perform experiments using both experimental and actual data for evaluating our algorithms. Consequently, our experimental results can be summarized as follows. First, our method showed a stable performance over existing PDF417 barcode detection and recognition. Second, it overcame the limitation problem where the location of an input image should locate in a predefined rectangle area. Finally, it is expected that our result can be used as a restoration tool of printed images such as documents and pictures. PDF417 (dpeaa)DE-He213 Barcode recognition (dpeaa)DE-He213 Morphology (dpeaa)DE-He213 Hough transform (dpeaa)DE-He213 Perspective transform (dpeaa)DE-He213 Lee, Jong Yun verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 89(2016), 3 vom: 14. Jan., Seite 893-911 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:89 year:2016 number:3 day:14 month:01 pages:893-911 https://dx.doi.org/10.1007/s11277-016-3171-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 89 2016 3 14 01 893-911
spelling	10.1007/s11277-016-3171-6 doi (DE-627)SPR018574955 (SPR)s11277-016-3171-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Kim, Young Jung verfasserin aut Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract When a PDF417 barcode are recognized, there are major recognition processes such as segmentation, normalization, and decoding. Among them, the segmentation and normalization steps are very important because they have a strong influence on the rate of barcode recognition. There are also previous segmentation and normalization techniques of processing barcode image, but some issues as follows. First, the previous normalization techniques need an additional restoration process and apply an interpolation process. Second, the previous recognition algorithms recognize a barcode image well only when it is placed in the predefined rectangular area. Therefore, we propose a novel segmentation and normalization method in PDF417 with the aims of improving its recognition rate and precision. The segmentation process to detect the barcode area in an image uses the conventional morphology and Hough transform methods. The normalization process of the bar code region is based on the conventional perspective transformation and warping algorithms. In addition, we perform experiments using both experimental and actual data for evaluating our algorithms. Consequently, our experimental results can be summarized as follows. First, our method showed a stable performance over existing PDF417 barcode detection and recognition. Second, it overcame the limitation problem where the location of an input image should locate in a predefined rectangle area. Finally, it is expected that our result can be used as a restoration tool of printed images such as documents and pictures. PDF417 (dpeaa)DE-He213 Barcode recognition (dpeaa)DE-He213 Morphology (dpeaa)DE-He213 Hough transform (dpeaa)DE-He213 Perspective transform (dpeaa)DE-He213 Lee, Jong Yun verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 89(2016), 3 vom: 14. Jan., Seite 893-911 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:89 year:2016 number:3 day:14 month:01 pages:893-911 https://dx.doi.org/10.1007/s11277-016-3171-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 89 2016 3 14 01 893-911
allfields_unstemmed	10.1007/s11277-016-3171-6 doi (DE-627)SPR018574955 (SPR)s11277-016-3171-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Kim, Young Jung verfasserin aut Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract When a PDF417 barcode are recognized, there are major recognition processes such as segmentation, normalization, and decoding. Among them, the segmentation and normalization steps are very important because they have a strong influence on the rate of barcode recognition. There are also previous segmentation and normalization techniques of processing barcode image, but some issues as follows. First, the previous normalization techniques need an additional restoration process and apply an interpolation process. Second, the previous recognition algorithms recognize a barcode image well only when it is placed in the predefined rectangular area. Therefore, we propose a novel segmentation and normalization method in PDF417 with the aims of improving its recognition rate and precision. The segmentation process to detect the barcode area in an image uses the conventional morphology and Hough transform methods. The normalization process of the bar code region is based on the conventional perspective transformation and warping algorithms. In addition, we perform experiments using both experimental and actual data for evaluating our algorithms. Consequently, our experimental results can be summarized as follows. First, our method showed a stable performance over existing PDF417 barcode detection and recognition. Second, it overcame the limitation problem where the location of an input image should locate in a predefined rectangle area. Finally, it is expected that our result can be used as a restoration tool of printed images such as documents and pictures. PDF417 (dpeaa)DE-He213 Barcode recognition (dpeaa)DE-He213 Morphology (dpeaa)DE-He213 Hough transform (dpeaa)DE-He213 Perspective transform (dpeaa)DE-He213 Lee, Jong Yun verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 89(2016), 3 vom: 14. Jan., Seite 893-911 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:89 year:2016 number:3 day:14 month:01 pages:893-911 https://dx.doi.org/10.1007/s11277-016-3171-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 89 2016 3 14 01 893-911
allfieldsGer	10.1007/s11277-016-3171-6 doi (DE-627)SPR018574955 (SPR)s11277-016-3171-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Kim, Young Jung verfasserin aut Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract When a PDF417 barcode are recognized, there are major recognition processes such as segmentation, normalization, and decoding. Among them, the segmentation and normalization steps are very important because they have a strong influence on the rate of barcode recognition. There are also previous segmentation and normalization techniques of processing barcode image, but some issues as follows. First, the previous normalization techniques need an additional restoration process and apply an interpolation process. Second, the previous recognition algorithms recognize a barcode image well only when it is placed in the predefined rectangular area. Therefore, we propose a novel segmentation and normalization method in PDF417 with the aims of improving its recognition rate and precision. The segmentation process to detect the barcode area in an image uses the conventional morphology and Hough transform methods. The normalization process of the bar code region is based on the conventional perspective transformation and warping algorithms. In addition, we perform experiments using both experimental and actual data for evaluating our algorithms. Consequently, our experimental results can be summarized as follows. First, our method showed a stable performance over existing PDF417 barcode detection and recognition. Second, it overcame the limitation problem where the location of an input image should locate in a predefined rectangle area. Finally, it is expected that our result can be used as a restoration tool of printed images such as documents and pictures. PDF417 (dpeaa)DE-He213 Barcode recognition (dpeaa)DE-He213 Morphology (dpeaa)DE-He213 Hough transform (dpeaa)DE-He213 Perspective transform (dpeaa)DE-He213 Lee, Jong Yun verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 89(2016), 3 vom: 14. Jan., Seite 893-911 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:89 year:2016 number:3 day:14 month:01 pages:893-911 https://dx.doi.org/10.1007/s11277-016-3171-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 89 2016 3 14 01 893-911
allfieldsSound	10.1007/s11277-016-3171-6 doi (DE-627)SPR018574955 (SPR)s11277-016-3171-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Kim, Young Jung verfasserin aut Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract When a PDF417 barcode are recognized, there are major recognition processes such as segmentation, normalization, and decoding. Among them, the segmentation and normalization steps are very important because they have a strong influence on the rate of barcode recognition. There are also previous segmentation and normalization techniques of processing barcode image, but some issues as follows. First, the previous normalization techniques need an additional restoration process and apply an interpolation process. Second, the previous recognition algorithms recognize a barcode image well only when it is placed in the predefined rectangular area. Therefore, we propose a novel segmentation and normalization method in PDF417 with the aims of improving its recognition rate and precision. The segmentation process to detect the barcode area in an image uses the conventional morphology and Hough transform methods. The normalization process of the bar code region is based on the conventional perspective transformation and warping algorithms. In addition, we perform experiments using both experimental and actual data for evaluating our algorithms. Consequently, our experimental results can be summarized as follows. First, our method showed a stable performance over existing PDF417 barcode detection and recognition. Second, it overcame the limitation problem where the location of an input image should locate in a predefined rectangle area. Finally, it is expected that our result can be used as a restoration tool of printed images such as documents and pictures. PDF417 (dpeaa)DE-He213 Barcode recognition (dpeaa)DE-He213 Morphology (dpeaa)DE-He213 Hough transform (dpeaa)DE-He213 Perspective transform (dpeaa)DE-He213 Lee, Jong Yun verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 89(2016), 3 vom: 14. Jan., Seite 893-911 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:89 year:2016 number:3 day:14 month:01 pages:893-911 https://dx.doi.org/10.1007/s11277-016-3171-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 89 2016 3 14 01 893-911
language	English
source	Enthalten in Wireless personal communications 89(2016), 3 vom: 14. Jan., Seite 893-911 volume:89 year:2016 number:3 day:14 month:01 pages:893-911
sourceStr	Enthalten in Wireless personal communications 89(2016), 3 vom: 14. Jan., Seite 893-911 volume:89 year:2016 number:3 day:14 month:01 pages:893-911
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	PDF417 Barcode recognition Morphology Hough transform Perspective transform
dewey-raw	620
isfreeaccess_bool	false
container_title	Wireless personal communications
authorswithroles_txt_mv	Kim, Young Jung @@aut@@ Lee, Jong Yun @@aut@@
publishDateDaySort_date	2016-01-14T00:00:00Z
hierarchy_top_id	271179120
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author	Kim, Young Jung
spellingShingle	Kim, Young Jung ddc 620 bkl 53.00 misc PDF417 misc Barcode recognition misc Morphology misc Hough transform misc Perspective transform Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition
authorStr	Kim, Young Jung
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topic_title	620 ASE 53.00 bkl Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition PDF417 (dpeaa)DE-He213 Barcode recognition (dpeaa)DE-He213 Morphology (dpeaa)DE-He213 Hough transform (dpeaa)DE-He213 Perspective transform (dpeaa)DE-He213
topic	ddc 620 bkl 53.00 misc PDF417 misc Barcode recognition misc Morphology misc Hough transform misc Perspective transform
topic_unstemmed	ddc 620 bkl 53.00 misc PDF417 misc Barcode recognition misc Morphology misc Hough transform misc Perspective transform
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title	Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition
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title_full	Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition
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title_sort	algorithm of a perspective transform-based pdf417 barcode recognition
title_auth	Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition
abstract	Abstract When a PDF417 barcode are recognized, there are major recognition processes such as segmentation, normalization, and decoding. Among them, the segmentation and normalization steps are very important because they have a strong influence on the rate of barcode recognition. There are also previous segmentation and normalization techniques of processing barcode image, but some issues as follows. First, the previous normalization techniques need an additional restoration process and apply an interpolation process. Second, the previous recognition algorithms recognize a barcode image well only when it is placed in the predefined rectangular area. Therefore, we propose a novel segmentation and normalization method in PDF417 with the aims of improving its recognition rate and precision. The segmentation process to detect the barcode area in an image uses the conventional morphology and Hough transform methods. The normalization process of the bar code region is based on the conventional perspective transformation and warping algorithms. In addition, we perform experiments using both experimental and actual data for evaluating our algorithms. Consequently, our experimental results can be summarized as follows. First, our method showed a stable performance over existing PDF417 barcode detection and recognition. Second, it overcame the limitation problem where the location of an input image should locate in a predefined rectangle area. Finally, it is expected that our result can be used as a restoration tool of printed images such as documents and pictures.
abstractGer	Abstract When a PDF417 barcode are recognized, there are major recognition processes such as segmentation, normalization, and decoding. Among them, the segmentation and normalization steps are very important because they have a strong influence on the rate of barcode recognition. There are also previous segmentation and normalization techniques of processing barcode image, but some issues as follows. First, the previous normalization techniques need an additional restoration process and apply an interpolation process. Second, the previous recognition algorithms recognize a barcode image well only when it is placed in the predefined rectangular area. Therefore, we propose a novel segmentation and normalization method in PDF417 with the aims of improving its recognition rate and precision. The segmentation process to detect the barcode area in an image uses the conventional morphology and Hough transform methods. The normalization process of the bar code region is based on the conventional perspective transformation and warping algorithms. In addition, we perform experiments using both experimental and actual data for evaluating our algorithms. Consequently, our experimental results can be summarized as follows. First, our method showed a stable performance over existing PDF417 barcode detection and recognition. Second, it overcame the limitation problem where the location of an input image should locate in a predefined rectangle area. Finally, it is expected that our result can be used as a restoration tool of printed images such as documents and pictures.
abstract_unstemmed	Abstract When a PDF417 barcode are recognized, there are major recognition processes such as segmentation, normalization, and decoding. Among them, the segmentation and normalization steps are very important because they have a strong influence on the rate of barcode recognition. There are also previous segmentation and normalization techniques of processing barcode image, but some issues as follows. First, the previous normalization techniques need an additional restoration process and apply an interpolation process. Second, the previous recognition algorithms recognize a barcode image well only when it is placed in the predefined rectangular area. Therefore, we propose a novel segmentation and normalization method in PDF417 with the aims of improving its recognition rate and precision. The segmentation process to detect the barcode area in an image uses the conventional morphology and Hough transform methods. The normalization process of the bar code region is based on the conventional perspective transformation and warping algorithms. In addition, we perform experiments using both experimental and actual data for evaluating our algorithms. Consequently, our experimental results can be summarized as follows. First, our method showed a stable performance over existing PDF417 barcode detection and recognition. Second, it overcame the limitation problem where the location of an input image should locate in a predefined rectangle area. Finally, it is expected that our result can be used as a restoration tool of printed images such as documents and pictures.
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container_issue	3
title_short	Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition
url	https://dx.doi.org/10.1007/s11277-016-3171-6
remote_bool	true
author2	Lee, Jong Yun
author2Str	Lee, Jong Yun
ppnlink	271179120
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s11277-016-3171-6
up_date	2024-07-03T20:41:24.537Z
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score	7.4019346

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Algorithm of a Perspective Transform-Based PDF417 Barcode Recognition

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