提出新数据集与多头注意力模型，提升孟加拉手写字符识别准确率。
Multi-Head Attention based interaction-aware architecture for Bangla Handwritten Character Recognition: Introducing a Primary Dataset
- 融合EfficientNetB3、ViT与Conformer的并行架构，用多头交叉注意力增强特征交互
- 在自建数据集上达98.84%准确率，在外部基准上达96.49%，泛化能力强
- 公开了78类、每类约650样本的平衡数据集，覆盖不同年龄与书写习惯人群
字符识别是光学字符识别（OCR）系统的基础。文字识别、句子转录、文档数字化和语言处理等高阶任务均依赖于精准的字符识别。然而，由于孟加拉手写字符风格多样、笔画不一致且视觉相似度高，识别难度大。现有数据集普遍存在类内差异小、类别分布不均的问题。为此，本文构建了一个新的均衡数据集，包含78个类别，每类约650个样本，涵盖基础字符、复合字符（Juktobarno）及数字。样本来自不同年龄段和社会经济背景的中小学生、大学生及专业人士，包含左右手书写者。同时提出一种交互感知的混合深度学习架构，将EfficientNetB3、Vision Transformer和Conformer模块并行集成，并通过多头交叉注意力机制实现组件间有效特征交互。该模型在自建数据集上达到98.84%准确率，在外部CHBCR基准上达96.49%，展现良好泛化能力。Grad-CAM可视化进一步揭示了模型关注的关键判别区域。相关数据集与源代码已公开于Hugging Face。
原文摘要 · Abstract (English)
Character recognition is the fundamental part of an optical character recognition (OCR) system. Word recognition, sentence transcription, document digitization, and language processing are some of the higher-order activities that can be done accurately through character recognition. Nonetheless, recognizing handwritten Bangla characters is not an easy task because they are written in different styles with inconsistent stroke patterns and a high degree of visual character resemblance. The datasets available are usually limited in intra-class and inequitable in class distribution. We have constructed a new balanced dataset of Bangla written characters to overcome those problems. This consists of 78 classes and each class has approximately 650 samples. It contains the basic characters, composite (Juktobarno) characters and numerals. The samples were a diverse group comprising a large age range and socioeconomic groups. Elementary and high school students, university students, and professionals are the contributing factors. The sample also has right and left-handed writers. We have further proposed an interaction-aware hybrid deep learning architecture that integrates EfficientNetB3, Vision Transformer, and Conformer modules in parallel. A multi-head cross-attention fusion mechanism enables effective feature interaction across these components. The proposed model achieves 98.84% accuracy on the constructed dataset and 96.49% on the external CHBCR benchmark, demonstrating strong generalization capability. Grad-CAM visualizations further provide interpretability by highlighting discriminative regions. The dataset and source code of this research is publicly available at: https://huggingface.co/MIRZARAQUIB/Bangla_Handwritten_Character_Recognition.
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