【摘要】 在复杂场景下，汉字识别面临挑战。由于字符可能被遮盖或变形，传统的特征提取或分类方法常常无法提取足够的特征，导致识别错误。此外，现有的视觉和语言结合的模型忽略了隐藏字符特征对语言模型的重要性，使得错误纠正不准确。为此，提出了一种基于字符、字根、关键笔形的多维表征识别算法。该算法结合自注意力机制提取多层次字符特征，有效解决了特征提取不足问题导致的识别错误问题。此外，构建了多维表征融合机制来连接视觉模型与语言模型，以有效传递隐藏字符特征给语言模型。算法分文本识别、识别字符检错、字符纠错三个阶段。实验结果表明，相较于基于Transformer架构的最先进模型，本文算法在场景文本、网页文本、印刷文本和手写文本数据集上的性能分别提升了1.81%、1.11%、0.25%和2.27%。更多还原

【Abstract】 In complex scenarios, Chinese character recognition encounters challenges. Traditional feature extraction or classification methods often fail to extract sufficient features due to potential character occlusion or deformation, leading to recognition errors. Furthermore, existing models that integrate visual and language information overlook the importance of hidden character features in the language model, resulting in inaccurate error correction. To tackle this issue, we propose a multi-dimensional representation recognition algorithm based on characters, radicals, and key pen strokes. This algorithm utilizes self-attention mechanisms to extract multi-level character features, effectively addressing the problem of insufficient feature extraction and reducing recognition errors. Additionally, a multi-dimensional representation fusion mechanism is developed to connect the visual model with the language model, effectively conveying hidden character features to the language model. This algorithm accomplishes text recognition, character recognition, and error correction in three stages. Experimental results demonstrate that compared to state-of-the-art Transformer-based models, the algorithm in this paper achieved performance improvements of 1.81%, 1.11%, 0.25%, and 2.27% on scene text, web datasets, printed text and handwritten datasets, respectively.更多还原