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              疊層模型驅動的書法文字識別方法研究

              麻斯亮 許勇

              麻斯亮, 許勇. 疊層模型驅動的書法文字識別方法研究. 自動化學報, 2024, 50(5): 947?957 doi: 10.16383/j.aas.c230460
              引用本文: 麻斯亮, 許勇. 疊層模型驅動的書法文字識別方法研究. 自動化學報, 2024, 50(5): 947?957 doi: 10.16383/j.aas.c230460
              Ma Si-Liang, Xu Yong. Calligraphy character recognition method driven by stacked model. Acta Automatica Sinica, 2024, 50(5): 947?957 doi: 10.16383/j.aas.c230460
              Citation: Ma Si-Liang, Xu Yong. Calligraphy character recognition method driven by stacked model. Acta Automatica Sinica, 2024, 50(5): 947?957 doi: 10.16383/j.aas.c230460

              疊層模型驅動的書法文字識別方法研究

              doi: 10.16383/j.aas.c230460
              基金項目: 國家自然科學基金(62072188)資助
              詳細信息
                作者簡介:

                麻斯亮:華南理工大學計算機科學與工程學院博士研究生. 主要研究方向為機器學習, 文字圖像處理. E-mail: 202010107394@mail.scut.edu.cn

                許勇:華南理工大學計算機科學與工程學院教授. 主要研究方向為機器學習, 視覺計算, 大數據. 本文通信作者. E-mail: yxu@scut.edu.cn

              Calligraphy Character Recognition Method Driven by Stacked Model

              Funds: Supported by National Natural Science Foundation of China (62072188)
              More Information
                Author Bio:

                MA Si-Liang Ph.D. candidate at the School of Computer Science and Engineering, South China University of Technology. His research interest covers machine learning and text image processing

                XU Yong Professor at the School of Computer Science and Engineering, South China University of Technology. His research interest covers machine learning, visual computing, and big data. Corresponding author of this paper

              • 摘要: 基于二維圖像的書法文字識別是指利用計算機視覺技術對書法文字單字圖像進行識別, 在古籍研究和文化傳播中具有重要應用. 目前書法文字識別技術已經取得了相當不錯的進展, 但依舊面臨很多挑戰, 比如復雜多變的字形可能導致的識別誤差, 漢字本身又存在較多形近字, 且漢字字符類別數與其他語言文字相比更多, 書法文字圖像普遍存在類內差距大、類間差距小的問題. 為解決這些問題, 提出疊層模型驅動的書法文字識別方法(Stacked-model driven character recognition, SDCR), 通過使用數據預處理、節點分離策略和疊層模型對現有單一分類模型進行改進, 按照字體類別對同一類別不同字體風格的文字進行二次劃分; 針對類間差距小的問題, 根據書法文字訓練集圖像識別置信度對形近字進行子集劃分, 針對子集進行嵌套模型增強訓練, 在測試階段利用疊層模型對形近字進行二次識別, 提升形近字的識別準確率. 為了驗證該方法的魯棒性, 在自主生成的SCUT_Calligraphy數據集和CASIA-HWDB 1.1, CASIA-AHCDB公開數據集上進行訓練和測試, 實驗結果表明該方法在上述數據集的識別準確率均有較大幅度提升, 在CASIA-HWDB 1.1、CASIA-AHCDB和自建數據集SCUT_Calligraphy上測試準確率分別達到96.33%、99.51%和99.90%, 證明了該方法的有效性.
              • 圖  1  中國書法作品樣例

                Fig.  1  Samples of Chinese calligraphy works

                圖  2  書法文字中同一類字不同字形及形近字示例

                Fig.  2  Examples of different glyphs and close shapes of the same type of characters in calligraphy text

                圖  3  本文所述部分數據集圖像示例

                Fig.  3  Part of images from datasets mentioned in this paper

                圖  4  疊層模型驅動的書法文字識別方法架構圖

                Fig.  4  Architecture of calligraphy character recognition method driven by stacked model

                圖  5  節點分離訓練策略流程圖(以“即”字為例)

                Fig.  5  Flowchart of nodes separation training strategy (Take the character “JI” as an example)

                圖  6  疊層模型驅動的書法文字識別測試階段流程圖

                Fig.  6  Flowchart of the test phase of calligraphy character recognition driven by stacked model

                圖  7  輸入圖像分辨率與書法文字識別準確率變化關系

                Fig.  7  The relationship between input image resolution and calligraphy character recognition accuracy

                表  1  實驗數據集詳細屬性

                Table  1  Detailed properties of experimental datasets

                數據集名稱類別數訓練集規模測試集規模
                CASIA-AHCDBStyle-1 BC2 353828 969253 990
                Style-1 EC3 20188 87036 143
                Style-2 BC2 353725 240202 404
                Style-2 EC74066 69017 741
                CASIA-HWDB 1.13 755847 466223 991
                SCUT_Calligraphy3 767251 66426 106
                下載: 導出CSV

                表  2  疊層模型驅動的書法文字識別消融實驗結果

                Table  2  Ablation experimental results of calligraphy character recognition driven by stacked model

                測試數據集數據預處理節點分離疊層模型驅動Precision (%)Recall (%)F1-Score (%)
                CASIA-HWDB 1.1×××89.6488.9589.29
                $\surd$××90.3489.3589.84
                $\surd$$\surd$×91.2689.5690.40
                $\surd$$\surd$$\surd$96.3392.1094.16
                CASIA-AHCDB (Style-1 BC)×××94.5095.1094.79
                $\surd$××98.9298.3498.62
                $\surd$$\surd$×99.1999.1499.16
                $\surd$$\surd$$\surd$99.5199.2199.35
                SCUT_Calligraphy×××91.3390.4590.88
                $\surd$××98.3898.2298.30
                $\surd$$\surd$×98.8598.3698.60
                $\surd$$\surd$$\surd$99.9098.9699.42
                下載: 導出CSV

                表  3  單模型和疊層模型驅動模型識別可視化結果對比

                Table  3  Comparison of visualization results for single model and stacked precision neural network model recognition

                輸入圖片標簽單模型預測值疊層模型預測值
                下載: 導出CSV

                表  4  不同子集書法文字圖像使用單模型和疊層模型驅動模型識別結果對比

                Table  4  Comparison of recognition results of different calligraphy character images subsets using single model and stacked model

                子集字符類別子集規模單模型錯誤數疊層模型錯誤數準確率提升(%)
                日目白自向冶治囚曰沼7411310.81
                大己已木犬片斤火本巳83532.40
                力工巾王勿古右布句希76946.57
                巨予主矛母吉臣吝圭毋86734.65
                夫云去央塵尖伏伐亥矢69727.24
                士土千比午北白自血皿76743.94
                去式戒赤坊束辰來妨展68727.35
                助忍駁玩抵忽振玖肋駿64744.68
                下載: 導出CSV

                表  5  不同方法在CASIA-AHCDB, CASIA-HWDB 1.1和SCUT_Calligraphy數據集上的測試結果對比 (%)

                Table  5  The performance of comparison different methods test on the CASIA-AHCDB, CASIA-HWDB 1.1 and SCUT_Calligraphy (%)

                方法數據集
                CASIA-AHCDBCASIA-HWDB 1.1SCUT_Calligraphy
                Style-1 BCStyle-1 BC&ECStyle-2 BCStyle-2 BC&ECStyle-1 BC (train) Style-2 BC (test)
                LW-ViT[34]95.80
                CPN[35]98.5096.9594.4291.9974.7495.4598.70
                RAN[36]82.3969.61
                RPN83.6569.63
                RAN + CRA[36]85.5471.02
                RPN + CRA[37]86.9172.06
                SDCR + JD99.5198.2398.7497.0186.1596.3399.90
                注: SDCR + JD指同時使用疊層模型驅動和節點分離訓練策略.
                下載: 導出CSV
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