ICDAR2019 Competition on Recognition of Early Indian Printed Documents – REID2019
Name
ICDAR2019_Competition_on_Recognition_of_Early_Indian_printed_Documents_-_REID2019.pdf
Description
visibility:open
Size
604.8 KB
Format
Adobe PDF
Checksum (CRC64NVME)
mRwqXRIHLTM=
Resource type
Conference paper (unpublished)
Creator (person)
Clausner, Christian
Antonacopoulos, Apostolos
Derrick, Tom
Pletschacher, Stefan
Date published
September 2019
Abstract
This paper presents an objective comparative evaluation of page analysis and recognition methods for historical documents with text mainly in Bengali language and script. It describes the competition rules, dataset, and evaluation methodology. Results are presented for five methods - three submit-ted, one re-run, and one open source state-of-the-art system. The focus is on optical character recognition (OCR) performance. Different evaluation metrics were used to gain an in-sight into the algorithms, including new character accuracy metrics to better reflect the difficult circumstances presented by the documents. The results indicate that deep learning approaches are promising, but there are still significant challenges for historic material of this nature.
Event title
2019 International Conference on Document Analysis and Recognition (ICDAR)
Publisher
IEEE
ISSN
2379-2140
eISSN
1520-5363
Official URL
Additional information
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.