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摘要: 开放集文字识别 (Open-set text recognition, OSTR) 是一项新任务, 旨在解决开放环境下文字识别应用中的语言模型偏差及新字符识别与拒识问题. 最近的 OSTR 方法通过将上下文信息与视觉信息分离来解决语言模型偏差问题. 然而, 这些方法往往忽视了字符视觉细节的重要性. 考虑到上下文信息的偏差, 局部细节信息在区分视觉上接近的字符时变得更加重要. 本文提出一种基于自适应字符部件表示的开放集文字识别框架, 构建基于文字局部结构相似度量的开放集文字识别方法, 通过对不同字符部件进行显式建模来改进对局部细节特征的建模能力. 与基于字根 (Radical) 的方法不同, 所提出的框架采用数据驱动的部件设计, 具有语言无关的特性和跨语言泛化识别的能力. 此外, 还提出一种局部性约束正则项来使模型训练更加稳定. 大量的对比实验表明, 本文方法在开放集、传统闭集文字识别任务上均具有良好的性能.Abstract: Open-set text recognition (OSTR) is an emerging task that aims to address language bias and novel characters in open-world text recognition applications. Recent OSTR methods have achieved some success by decoupling the potentially biased context information with visual information. However, they tend to overlook the increasing importance of visual details. Given the biases in contextual information, detailed visual information became much more important in differentiating visually close characters. This work proposes an adaptive part-representation-based open-set text recognition framework and an open-set text recognition method via part-based similarly to improve the visual details modeling by explicitly modeling different character parts. Unlike radical-based methods, the proposed framework adopts a data-driven parting scheme, hence is language agnostic. A localization constraint is further proposed to address the instability caused by the parting scheme. The full framework steadily outperforms its baseline and yields reasonable performance on the close-set benchmarks.1)
1 1 代码, 模型, 文档见:https://github.com/lancercat/OAPR 2)2 2 注意, 字符在特征空间的区域可能有交集. -
图 1 基于整字符识别方法的形近字混淆
Fig. 1 The confusion among close characters of the whole-character-based method
图 3 本文提出的基于自适应字符部件表示的开放集文字识别框架
Fig. 3 The proposed open-set text recognition framework with adaptive part representation
图 8 基线方法(上侧) 与我们的模型 (下侧) 的识别结果对比
Fig. 8 More comparison between base method (top) and the proposed framework (bottom)
图 9 日文测试数据集上的识别结果(GZSL 划分)
Fig. 9 Sample results from the Japanese testing data set (With GZSL split)
表 1 消融实验
Table 1 Ablative studies
自适应字符
部件表示局部性
约束Avg LA $ \uparrow $ Gap LA $ \downarrow $ Ours $\checkmark $ $\checkmark $ 39.61 4.91 仅自适应字符部件表示 $\checkmark $ 38.91 6.54 字符整体特征 34.04 2.27 
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表 2 开放集文字识别性能
Table 2 Performance on open-set text recognition benchmarks
任务 $ {\boldsymbol{C}}_{test}^k $ $ {\boldsymbol{C}}_{test}^u $ 方法 来源 LA (%) Recall (%) Precision (%) F-measure (%) Unique Kanji OSOCR-Large[8] PR＇ 2023 30.83 — — — GZSL Shared Kanji $ \emptyset $ OpenCCD[9] CVPR＇ 2022 36.57 — — — Kana, Latin OpenCCD-Large[9] CVPR＇ 2022 41.31 — — — Ours — 39.61 — — — Ours-Large — 40.91 — — — OSR Shared Kanji Unique Kanji OSOCR-Large[8] PR＇ 2023 74.35 11.27 98.28 20.23 Latin Kana OpenCCD-Large*[9] CVPR＇ 2022 84.76 30.63 98.90 46.78 Ours — 73.56 64.30 96.21 76.66 Ours-Large — 77.15 60.59 96.80 74.52 GOSR Shared Kanji Kana OSOCR-Large[8] PR＇ 2023 56.03 3.03 63.52 5.78 Unique Kanji OpenCCD-Large*[9] CVPR＇ 2022 68.29 3.47 86.11 6.68 Latin Ours — 65.07 54.12 82.52 64.65 Ours-Large — 67.40 47.64 82.99 60.53 OSTR Shared Kanji Kana OSOCR-Large[8] PR＇ 2023 58.57 24.46 93.78 38.80 Unique Kanji Latin OpenCCD-Large*[9] CVPR＇ 2022 69.82 35.95 97.03 52.47 Ours — 68.20 81.04 89.86 85.07 Ours-Large — 69.87 75.97 91.18 82.88 注: * 表示原论文中未报告的性能, 数据来自原作者代码仓库和释出的模型.
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表 3 封闭集文字识别基准测试性能及单批次推理速度
Table 3 Performance on close-set text recognition benchmarks and single batch inference speed
方法 来源 IIIT5K CUTE SVT IC03 IC13 GPU TFlops FPS CA-FCN*[22] AAAI＇19 92.0 79.9 82.1 — 91.4 Titan XP 12.0 45.0 Comb.Best[23] ICCV＇19 87.9 74.0 87.5 94.4 92.3 Tesla P40 12.0 36.0 PERN[47] CVPR＇21 92.1 81.3 92.0 94.9 94.7 Tesla V100 14.0 44.0 JVSR[48] ICCV＇21 95.2 89.7 92.2 — 95.5 RTX 2080Ti 13.6 38.0 ABINet[49] T-PAMI＇23 96.2 89.2 93.5 97.4 95.7 V100 14.0 29.4 CRNN[21, 23] T-PAMI＇17 82.9 65.5 81.6 92.6 89.2 Tesla P40 12.0 227.0 Rosetta[23, 50] KDD＇18 84.3 69.2 84.7 92.9 89.0 Tesla P40 12.0 212.0 ViTSTR[51] ICDAR＇21 88.4 81.3 87.7 94.3 92.4 RTX 2080Ti 13.6 102.0 GLaLT-Big-Aug[52] TNNLS＇23 90.4 77.1 90.0 95.2 95.3 — — 62.1 Ours-Large — 89.06 77.77 80.68 89.61 87.98 Tesla P40 12.0 85.7
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