Handwriting recognition is an important area in pattern recognition. For many years, Tai Le has been widely used in Southwest China and Southeast Asia, which makes it of great interest for recognition research. The characteristics of the highly similar characters in Tai Le, such as its large proportion of similar characters and the randomness of its writing, bring great challenges to the task of recognition. In this paper, a method based on ensemble deep learning for offline handwritten Tai Le characters is proposed. First, the handwritten Tai Le character dataset SDH2019.2 was constructed and preprocessed. Then, an ensemble deep convolutional neural network (EDCNN) model was constructed by using a stacking strategy. Thirty deep neural network (DNN) and logistic regression algorithms were integrated into a strong Tai Le classifier by stacking. Experiments showed that the proposed model is competitive with the base DNN model and other ensemble models. The results indicate that the performance of Tai Le recognition by the stacking ensemble-based deep neural network model is high, with an accuracy of 98.85%. Additionally, its precision, recall and F1-score of 98.87%, 98.85% and 98.85%, respectively, are superior to those of other classic neural network models. To verify the general applicability of EDCNN, its effectiveness was also verified by recognizing MNIST handwritten digits and Devanagari handwritten characters.

手写识别是模式识别的一个重要领域。多年来，太乐在西南地区和东南亚地区得到了广泛的应用，这使得它对识别研究产生了极大的兴趣。太乐中高度相似字的特征，如相似字比例大、书写随意性等，给识别任务带来了很大的挑战。在本文中，提出了一种基于集成深度学习的离线手写太乐字符方法。首先，构建并预处理了手写太乐字符数据集SDH2019.2。然后，使用堆叠策略构建集成深度卷积神经网络（EDCNN）模型。通过堆叠将三十个深度神经网络 (DNN) 和逻辑回归算法集成到一个强大的太乐分类器中。实验表明，所提出的模型与基础 DNN 模型和其他集成模型具有竞争力。结果表明，基于stacking ensemble的深度神经网络模型对太乐的识别性能较高，准确率为98.85%。此外，其准确率、召回率和 F1-score 分别为 98.87%、98.85% 和 98.85%，优于其他经典神经网络模型。为了验证 EDCNN 的普遍适用性，还通过识别 MNIST 手写数字和梵文手写字符来验证其有效性。其准确率、召回率和 F1-score 分别达到 98.87%、98.85% 和 98.85%，优于其他经典神经网络模型。为了验证 EDCNN 的普遍适用性，还通过识别 MNIST 手写数字和梵文手写字符来验证其有效性。其准确率、召回率和 F1-score 分别达到 98.87%、98.85% 和 98.85%，优于其他经典神经网络模型。为了验证 EDCNN 的普遍适用性，还通过识别 MNIST 手写数字和梵文手写字符来验证其有效性。