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ICDAR 2019 Historical Document Reading Challenge on Large Structured ChineseFamily Records
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Lulea University of Technology, Lulea, Sweden. (Machine Learning)
FamilySearch, USA.
FamilySearch, USA.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. (Machine Learning)
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2019 (English)In: ICDAR 2019: ICDAR 2019 HDRC Chinese, 2019Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a large historical database of Chinese family records with the aim to develop robust systems for historical document analysis. In this direction, we propose a Historical Document Reading Challenge on Large Chinese Structured Family Records (ICDAR 2019 HDRCCHINESE).The objective of the competition is to recognizeand analyze the layout, and finally detect and recognize thetextlines and characters of the large historical document image dataset containing more than 10000 pages. Cascade R-CNN, CRNN, and U-Net based architectures were trained to evaluatethe performances in these tasks. Error rate of 0.01 has been recorded for textline recognition (Task1) whereas a Jaccard Index of 99.54% has been recorded for layout analysis (Task2).The graph edit distance based total error ratio of 1.5% has been recorded for complete integrated textline detection andrecognition (Task3).

Place, publisher, year, edition, pages
2019.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Identifiers
URN: urn:nbn:se:ltu:diva-77258OAI: oai:DiVA.org:ltu-77258DiVA, id: diva2:1381760
Conference
ICDAR 2019
Available from: 2019-12-27 Created: 2019-12-27 Last updated: 2019-12-27

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