UniText: A Unified Framework for Chinese Text Detection, Recognition, and Restoration in Ancient Document and Inscription Images Article Swipe
Lu Shen
,
Zewei Wu
,
Xiaoyuan Huang
,
Boliang Zhang
,
Su-Kit Tang
,
J. Henriques
,
Silvia Mirri
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15147662
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15147662
Processing ancient text images presents significant challenges due to severe visual degradation, missing glyph structures, and various types of noise caused by aging. These issues are particularly prominent in Chinese historical documents and stone inscriptions, where diverse writing styles, multi-angle capturing, uneven lighting, and low contrast further hinder the performance of traditional OCR techniques. In this paper, we propose a unified neural framework, UniText, for the detection, recognition, and glyph restoration of Chinese characters in images of historical documents and inscriptions. UniText operates at the character level and processes full-page inputs, making it robust to multi-scale, multi-oriented, and noise-corrupted text. The model adopts a multi-task architecture that integrates spatial localization, semantic recognition, and visual restoration through stroke-aware supervision and multi-scale feature aggregation. Experimental results on our curated dataset of ancient Chinese texts demonstrate that UniText achieves a competitive performance in detection and recognition while producing visually faithful restorations under challenging conditions. This work provides a technically scalable and generalizable framework for image-based document analysis, with potential applications in historical document processing, digital archiving, and broader tasks in text image understanding.
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- en
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- https://doi.org/10.3390/app15147662
- https://www.mdpi.com/2076-3417/15/14/7662/pdf?version=1751983393
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- OpenAlex ID
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UniText: A Unified Framework for Chinese Text Detection, Recognition, and Restoration in Ancient Document and Inscription ImagesWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-07-08Full publication date if available
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Lu Shen, Zewei Wu, Xiaoyuan Huang, Boliang Zhang, Su-Kit Tang, J. Henriques, Silvia MirriList of authors in order
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https://doi.org/10.3390/app15147662Publisher landing page
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/15/14/7662/pdf?version=1751983393Direct OA link when available
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Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.on | 124 |
| abstract_inverted_index.to | 8, 94 |
| abstract_inverted_index.we | 57 |
| abstract_inverted_index.OCR | 52 |
| abstract_inverted_index.The | 100 |
| abstract_inverted_index.and | 15, 32, 43, 68, 79, 87, 97, 112, 118, 141, 157, 173 |
| abstract_inverted_index.are | 25 |
| abstract_inverted_index.due | 7 |
| abstract_inverted_index.for | 64, 160 |
| abstract_inverted_index.low | 44 |
| abstract_inverted_index.our | 125 |
| abstract_inverted_index.the | 48, 65, 84 |
| abstract_inverted_index.This | 151 |
| abstract_inverted_index.text | 2, 177 |
| abstract_inverted_index.that | 106, 133 |
| abstract_inverted_index.this | 55 |
| abstract_inverted_index.with | 164 |
| abstract_inverted_index.work | 152 |
| abstract_inverted_index.These | 23 |
| abstract_inverted_index.glyph | 13, 69 |
| abstract_inverted_index.image | 178 |
| abstract_inverted_index.level | 86 |
| abstract_inverted_index.model | 101 |
| abstract_inverted_index.noise | 19 |
| abstract_inverted_index.stone | 33 |
| abstract_inverted_index.tasks | 175 |
| abstract_inverted_index.text. | 99 |
| abstract_inverted_index.texts | 131 |
| abstract_inverted_index.types | 17 |
| abstract_inverted_index.under | 148 |
| abstract_inverted_index.where | 35 |
| abstract_inverted_index.while | 143 |
| abstract_inverted_index.adopts | 102 |
| abstract_inverted_index.aging. | 22 |
| abstract_inverted_index.caused | 20 |
| abstract_inverted_index.hinder | 47 |
| abstract_inverted_index.images | 3, 75 |
| abstract_inverted_index.issues | 24 |
| abstract_inverted_index.making | 91 |
| abstract_inverted_index.neural | 61 |
| abstract_inverted_index.paper, | 56 |
| abstract_inverted_index.robust | 93 |
| abstract_inverted_index.severe | 9 |
| abstract_inverted_index.uneven | 41 |
| abstract_inverted_index.visual | 10, 113 |
| abstract_inverted_index.Chinese | 29, 72, 130 |
| abstract_inverted_index.UniText | 81, 134 |
| abstract_inverted_index.ancient | 1, 129 |
| abstract_inverted_index.broader | 174 |
| abstract_inverted_index.curated | 126 |
| abstract_inverted_index.dataset | 127 |
| abstract_inverted_index.digital | 171 |
| abstract_inverted_index.diverse | 36 |
| abstract_inverted_index.feature | 120 |
| abstract_inverted_index.further | 46 |
| abstract_inverted_index.inputs, | 90 |
| abstract_inverted_index.missing | 12 |
| abstract_inverted_index.propose | 58 |
| abstract_inverted_index.results | 123 |
| abstract_inverted_index.spatial | 108 |
| abstract_inverted_index.styles, | 38 |
| abstract_inverted_index.through | 115 |
| abstract_inverted_index.unified | 60 |
| abstract_inverted_index.various | 16 |
| abstract_inverted_index.writing | 37 |
| abstract_inverted_index.UniText, | 63 |
| abstract_inverted_index.achieves | 135 |
| abstract_inverted_index.contrast | 45 |
| abstract_inverted_index.document | 162, 169 |
| abstract_inverted_index.faithful | 146 |
| abstract_inverted_index.operates | 82 |
| abstract_inverted_index.presents | 4 |
| abstract_inverted_index.provides | 153 |
| abstract_inverted_index.scalable | 156 |
| abstract_inverted_index.semantic | 110 |
| abstract_inverted_index.visually | 145 |
| abstract_inverted_index.analysis, | 163 |
| abstract_inverted_index.character | 85 |
| abstract_inverted_index.detection | 140 |
| abstract_inverted_index.documents | 31, 78 |
| abstract_inverted_index.framework | 159 |
| abstract_inverted_index.full-page | 89 |
| abstract_inverted_index.lighting, | 42 |
| abstract_inverted_index.potential | 165 |
| abstract_inverted_index.processes | 88 |
| abstract_inverted_index.producing | 144 |
| abstract_inverted_index.prominent | 27 |
| abstract_inverted_index.Processing | 0 |
| abstract_inverted_index.archiving, | 172 |
| abstract_inverted_index.capturing, | 40 |
| abstract_inverted_index.challenges | 6 |
| abstract_inverted_index.characters | 73 |
| abstract_inverted_index.detection, | 66 |
| abstract_inverted_index.framework, | 62 |
| abstract_inverted_index.historical | 30, 77, 168 |
| abstract_inverted_index.integrates | 107 |
| abstract_inverted_index.multi-task | 104 |
| abstract_inverted_index.challenging | 149 |
| abstract_inverted_index.competitive | 137 |
| abstract_inverted_index.conditions. | 150 |
| abstract_inverted_index.demonstrate | 132 |
| abstract_inverted_index.image-based | 161 |
| abstract_inverted_index.multi-angle | 39 |
| abstract_inverted_index.multi-scale | 119 |
| abstract_inverted_index.performance | 49, 138 |
| abstract_inverted_index.processing, | 170 |
| abstract_inverted_index.recognition | 142 |
| abstract_inverted_index.restoration | 70, 114 |
| abstract_inverted_index.significant | 5 |
| abstract_inverted_index.structures, | 14 |
| abstract_inverted_index.supervision | 117 |
| abstract_inverted_index.technically | 155 |
| abstract_inverted_index.techniques. | 53 |
| abstract_inverted_index.traditional | 51 |
| abstract_inverted_index.Experimental | 122 |
| abstract_inverted_index.aggregation. | 121 |
| abstract_inverted_index.applications | 166 |
| abstract_inverted_index.architecture | 105 |
| abstract_inverted_index.degradation, | 11 |
| abstract_inverted_index.multi-scale, | 95 |
| abstract_inverted_index.particularly | 26 |
| abstract_inverted_index.recognition, | 67, 111 |
| abstract_inverted_index.restorations | 147 |
| abstract_inverted_index.stroke-aware | 116 |
| abstract_inverted_index.generalizable | 158 |
| abstract_inverted_index.inscriptions, | 34 |
| abstract_inverted_index.inscriptions. | 80 |
| abstract_inverted_index.localization, | 109 |
| abstract_inverted_index.understanding. | 179 |
| abstract_inverted_index.multi-oriented, | 96 |
| abstract_inverted_index.noise-corrupted | 98 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.2697046 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |