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深層学習による文書画像の領域分割およびラベル生成ツールASLA における領域分割の精度向上と適用範囲の拡大のための拡張
https://doi.org/10.34481/0002000821
https://doi.org/10.34481/000200082185c4c7f3-12b5-4576-b432-6eff22dc4921
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
本文 (2.2 MB)
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||||||||
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| 公開日 | 2024-10-23 | |||||||||||
| タイトル | ||||||||||||
| タイトル | 深層学習による文書画像の領域分割およびラベル生成ツールASLA における領域分割の精度向上と適用範囲の拡大のための拡張 | |||||||||||
| 言語 | ja | |||||||||||
| タイトル | ||||||||||||
| タイトル | Extension to Improve the Performance of Region Segmentation and Expand the Scope in ASLA Which Is a Segmentation and Labeling Tool for Document Images Based on Deep Learning | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | jpn | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | region segmentation | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | labeling | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | document image | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | rule-based region redividing | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| 資源タイプ | departmental bulletin paper | |||||||||||
| ID登録 | ||||||||||||
| ID登録 | 10.34481/0002000821 | |||||||||||
| ID登録タイプ | JaLC | |||||||||||
| アクセス権 | ||||||||||||
| アクセス権 | open access | |||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||
| 著者 |
柿木, 幹太
× 柿木, 幹太
× 片山, 徹郎 |
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | The current situation of the electronic documents is only a substitute for paper. As a new way to utilize electronic documents, we focus on dividing electronic documents into regions by their elements and generating keywords and sentences as labels from the contents of the elements. However, these tasks, when performed manually, are timeconsuming and labor-intensive. To reduce the time required for region segmentation and label generation, we developed a prototype of ASLA, a tool for region segmentation and label generation of document images using deep learning in our laboratory. However, the existing ASLA has some problems in terms of accuracy of region segmentation and is not highly useful. Therefore, this study expands the existing ASLA to improve its usefulness. Specifically, first, a rule-based region redividing process is added. And then, the object detection method used in segmentation of ASLA is modified. Furthermore, the dataset is added. As application and evaluation results, we have confirmed that the usefulness of the expanded ASLA is improved. | |||||||||||
| 言語 | en | |||||||||||
| bibliographic_information |
ja : 宮崎大学工学部紀要 en : Memoirs of Faculty of Engineering, University of Miyazaki 巻 53, p. 83-88, 発行日 2024-10-23 |
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| 出版者 | ||||||||||||
| 出版者 | 宮崎大学工学部 | |||||||||||
| 言語 | ja | |||||||||||
| 出版者 | ||||||||||||
| 出版者 | Faculty of Engineering, University of Miyazaki | |||||||||||
| 言語 | en | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | PISSN | |||||||||||
| 収録物識別子 | 05404924 | |||||||||||
| item_10002_source_id_11 | ||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||
| 収録物識別子 | AA00732558 | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
