标题： 孟加拉手写字符和数字识别的少样本学习方法性能分析 显示英文标题

标题： Performance Analysis of Few-Shot Learning Approaches for Bangla Handwritten Character and Digit Recognition

摘要： 本研究调查了少量学习（FSL）方法在使用有限标记数据识别孟加拉语手写字符和数字时的表现。 它展示了这些方法在具有复杂结构的脚本中的适用性，其中数据集稀缺是一个常见的挑战。 鉴于孟加拉语脚本的复杂性，我们假设在这些字符上表现良好的模型可以有效推广到结构相似或更低复杂度的语言。 为此，我们引入了SynergiProtoNet，这是一种混合网络，旨在提高手写字符和数字识别的准确性。 该模型结合了先进的聚类技术与强大的嵌入框架，以捕获细微细节和上下文细微差别。 它在一个原型学习框架内利用多级（高阶和低阶）特征提取。 我们严格对比了几种最先进的少量学习模型： BD-CSPN、原型网络、关系网络、匹配网络和SimpleShot，在包括单语言数据集内评估、单语言跨数据集评估、跨语言迁移和分割数字测试在内的多种评估设置下。 实验结果表明，SynergiProtoNet始终优于现有方法，在手写字符和数字识别的少量学习中建立了新的基准。 代码可在GitHub上获取：https://github.com/MehediAhamed/SynergiProtoNet。 显示英文摘要

摘要： This study investigates the performance of few-shot learning (FSL) approaches in recognizing Bangla handwritten characters and numerals using limited labeled data. It demonstrates the applicability of these methods to scripts with intricate and complex structures, where dataset scarcity is a common challenge. Given the complexity of Bangla script, we hypothesize that models performing well on these characters can generalize effectively to languages of similar or lower structural complexity. To this end, we introduce SynergiProtoNet, a hybrid network designed to improve the recognition accuracy of handwritten characters and digits. The model integrates advanced clustering techniques with a robust embedding framework to capture fine-grained details and contextual nuances. It leverages multi-level (both high- and low-level) feature extraction within a prototypical learning framework. We rigorously benchmark SynergiProtoNet against several state-of-the-art few-shot learning models: BD-CSPN, Prototypical Network, Relation Network, Matching Network, and SimpleShot, across diverse evaluation settings including Monolingual Intra-Dataset Evaluation, Monolingual Inter-Dataset Evaluation, Cross-Lingual Transfer, and Split Digit Testing. Experimental results show that SynergiProtoNet consistently outperforms existing methods, establishing a new benchmark in few-shot learning for handwritten character and digit recognition. The code is available on GitHub: https://github.com/MehediAhamed/SynergiProtoNet.