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Title: Towards Automated Transcription of Label Text from Pinned Insect Collections

Abstract

We present a computer vision system that can transcribe the text on tiny printed labels stacked beneath pinned insects (as found in museum collections). The approach uses multiple views of each label because the labels are often occluded by the pin, the insect specimen, and other labels. Our approach handles occlusion and the extreme viewing angles required to image the stacked labels. Automated image analysis identifies the lines of text and then aligns and rectifies the images. Combining the aligned and rectified images from multiple viewpoints enables us to create a composite image that can be read using optical character recognition tools (OCR) to extract the text. We provide experimental demonstration using both museum specimens and experimental test labels

Authors:
; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
Argonne National Laboratory - Laboratory Directed Research and Development (LDRD)
OSTI Identifier:
1460741
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Conference
Resource Relation:
Conference: 2018 IEEE Winter Conference on Applications of Computer Vision, 03/12/18 - 03/14/18, Lake Tahoe, NV, US
Country of Publication:
United States
Language:
English

Citation Formats

Agarwal, Nitin, Ferrier, Nicola, and Hereld, Mark. Towards Automated Transcription of Label Text from Pinned Insect Collections. United States: N. p., 2018. Web. doi:10.1109/WACV.2018.00027.
Agarwal, Nitin, Ferrier, Nicola, & Hereld, Mark. Towards Automated Transcription of Label Text from Pinned Insect Collections. United States. doi:10.1109/WACV.2018.00027.
Agarwal, Nitin, Ferrier, Nicola, and Hereld, Mark. Mon . "Towards Automated Transcription of Label Text from Pinned Insect Collections". United States. doi:10.1109/WACV.2018.00027.
@article{osti_1460741,
title = {Towards Automated Transcription of Label Text from Pinned Insect Collections},
author = {Agarwal, Nitin and Ferrier, Nicola and Hereld, Mark},
abstractNote = {We present a computer vision system that can transcribe the text on tiny printed labels stacked beneath pinned insects (as found in museum collections). The approach uses multiple views of each label because the labels are often occluded by the pin, the insect specimen, and other labels. Our approach handles occlusion and the extreme viewing angles required to image the stacked labels. Automated image analysis identifies the lines of text and then aligns and rectifies the images. Combining the aligned and rectified images from multiple viewpoints enables us to create a composite image that can be read using optical character recognition tools (OCR) to extract the text. We provide experimental demonstration using both museum specimens and experimental test labels},
doi = {10.1109/WACV.2018.00027},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Conference:
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