Accurate currency recognition is essential for assistive technologies, particularly for visually impaired individuals who rely on others to identify banknotes. This dependency puts them at risk of fraud and exploitation. To address these challenges, we first build a new Bangladeshi banknote dataset that includes both controlled and real-world scenarios, ensuring a more comprehensive and diverse representation. Next, to enhance the dataset's robustness, we incorporate four additional datasets, including public benchmarks, to cover various complexities and improve the model's generalization. To overcome the limitations of current recognition models, we propose a novel hybrid CNN architecture that combines MobileNetV3-Large and EfficientNetB0 for efficient feature extraction. This is followed by an effective multilayer perceptron (MLP) classifier to improve performance while keeping computational costs low, making the system suitable for resource-constrained devices. The experimental results show that the proposed model achieves 97.95% accuracy on controlled datasets, 92.84% on complex backgrounds, and 94.98% accuracy when combining all datasets. The model's performance is thoroughly evaluated using five-fold cross-validation and seven metrics: accuracy, precision, recall, F1-score, Cohen's Kappa, MCC, and AUC. Additionally, explainable AI methods like LIME and SHAP are incorporated to enhance transparency and interpretability.

翻译：准确的货币识别对于辅助技术至关重要，尤其对于依赖他人识别纸币的视障人士而言。这种依赖性使他们面临欺诈和剥削的风险。为应对这些挑战，我们首先构建了一个新的孟加拉国纸币数据集，包含受控场景和真实场景，以确保更全面和多样化的代表性。其次，为增强数据集的鲁棒性，我们整合了包括公共基准在内的四个额外数据集，以覆盖各种复杂性并提升模型的泛化能力。为克服现有识别模型的局限性，我们提出了一种新颖的混合CNN架构，该架构结合MobileNetV3-Large和EfficientNetB0以实现高效特征提取。随后采用一个有效的多层感知机（MLP）分类器，在保持较低计算成本的同时提升性能，使系统适用于资源受限设备。实验结果表明，所提模型在受控数据集上达到97.95%的准确率，在复杂背景下达到92.84%的准确率，在整合所有数据集时达到94.98%的准确率。该模型的性能通过五折交叉验证和七项指标进行了全面评估：准确率、精确率、召回率、F1分数、Cohen's Kappa、MCC和AUC。此外，我们整合了LIME和SHAP等可解释AI方法，以增强透明度和可解释性。