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A knowledge-based recognition system for historical Mongolian documents

Authors:

Feilong BaoAuthors Info & Claims

International Journal on Document Analysis and Recognition, Volume 19, Issue 3

Pages 221 - 235

https://doi.org/10.1007/s10032-016-0267-1

Published: 01 September 2016 Publication History

Abstract

This paper proposes a knowledge-based system to recognize historical Mongolian documents in which the words exhibit remarkable variation and character overlapping. According to the characteristics of Mongolian word formation, the system combines a holistic scheme and a segmentation-based scheme for word recognition. Several types of words and isolated suffixes that cannot be segmented into glyph-units or do not require segmentation are recognized using the holistic scheme. The remaining words are recognized using the segmentation-based scheme, which is the focus of this paper. We exploit the knowledge of the glyph characteristics to segment words into glyph-units in the segmentation-based scheme. Convolutional neural networks are employed not only for word recognition in the holistic scheme, but also for glyph-unit recognition in the segmentation-based scheme. Based on the analysis of recognition errors in the segmentation-based scheme, the system is enhanced by integrating three strategies into glyph-unit recognition. These strategies involve incorporating baseline information, glyph-unit grouping, and recognizing under-segmented and over-segmented fragments. The proposed system achieves 80.86 % word accuracy on the Mongolian Kanjur test samples.

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Cited By

Wang WBao FZhang H(2024)Segmentation-Free Todo Mongolian OCR and its Public DatasetPattern Recognition and Computer Vision10.1007/978-981-97-8511-7_6(72-85)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1007/978-981-97-8511-7_6
Sun SWei HWang YHe C(2024)A Multi-feature Fusion Approach for Words Recognition of Ancient Mongolian DocumentsPattern Recognition and Computer Vision10.1007/978-981-97-8511-7_24(334-347)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1007/978-981-97-8511-7_24
Waqas MAnjum NAfzal M(2023)A hybrid strategy to extract metadata from scholarly articles by utilizing support vector machine and heuristicsScientometrics10.1007/s11192-023-04774-7128:8(4349-4382)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s11192-023-04774-7
Show More Cited By

A knowledge-based recognition system for historical Mongolian documents
1. Applied computing
  1. Document management and text processing
    1. Document capture
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems

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Published In

cover image International Journal on Document Analysis and Recognition

International Journal on Document Analysis and Recognition Volume 19, Issue 3

September 2016

93 pages

ISSN:1433-2833

EISSN:1433-2825

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Copyright © Copyright © 2016 Springer-Verlag Berlin Heidelberg.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 September 2016

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Cited By

Wang WBao FZhang H(2024)Segmentation-Free Todo Mongolian OCR and its Public DatasetPattern Recognition and Computer Vision10.1007/978-981-97-8511-7_6(72-85)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1007/978-981-97-8511-7_6
Sun SWei HWang YHe C(2024)A Multi-feature Fusion Approach for Words Recognition of Ancient Mongolian DocumentsPattern Recognition and Computer Vision10.1007/978-981-97-8511-7_24(334-347)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1007/978-981-97-8511-7_24
Waqas MAnjum NAfzal M(2023)A hybrid strategy to extract metadata from scholarly articles by utilizing support vector machine and heuristicsScientometrics10.1007/s11192-023-04774-7128:8(4349-4382)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s11192-023-04774-7
Cui SSu YQing dao er ji RJi Y(2022)An end-to-end network for irregular printed Mongolian recognitionInternational Journal on Document Analysis and Recognition10.1007/s10032-021-00388-y25:1(41-50)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10032-021-00388-y
Wei HLiu KZhang JFan D(2021)Data Augmentation Based on CycleGAN for Improving Woodblock-Printing Mongolian Words RecognitionDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86337-1_35(526-537)Online publication date: 5-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-86337-1_35
Wei HLiu CZhang HBao FGao G(2019)End-to-End Model for Offline Handwritten Mongolian Word RecognitionNatural Language Processing and Chinese Computing10.1007/978-3-030-32236-6_19(220-230)Online publication date: 9-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-32236-6_19

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