用迁移学习实现高精度孟加拉手写字符分类
Isolated Bangla Handwritten Character Classification using Transfer Learning
- 基于3D CNN、ResNet和MobileNet的迁移学习方法
- 在84类字符上达99.46%测试准确率
- 适合需要高精度手写文字识别的研究者
孟加拉语包含50个基本字符及大量复合字符。本研究采用迁移学习方法，对孟加拉手写字符（包括基本与复合字符）进行端到端分类，并有效缓解梯度消失问题。使用3D卷积神经网络（3DCNN）、残差网络（ResNet）和MobileNet等深度神经网络技术，在包含166,105张图像样本的Bangla Lekha Isolated数据集上进行训练，该数据集分为84个独立类别。模型在训练集上达到99.82%准确率，在测试集上达到99.46%准确率。与多项前沿基准相比，本模型在分类性能上表现更优。
原文摘要 · Abstract (English)
Bangla language consists of fifty distinct characters and many compound characters. Several notable studies have been performed to recognize Bangla characters, both handwritten and optical. Our approach uses transfer learning to classify the basic, distinct, as well as compound Bangla handwritten characters while avoiding the vanishing gradient problem. Deep Neural Network techniques such as 3D Convolutional Neural Network (3DCNN), Residual Neural Network (ResNet), and MobileNet are applied to generate an end-to-end classification of all possible standard formations of handwritten characters in the Bangla language. The Bangla Lekha Isolated dataset, which contains 166,105 Bangla character image samples categorized into 84 distinct classes, is used for this classification model. The model achieved 99.82% accuracy on training data and 99.46% accuracy on test data. Comparisons with various state-of-the-art benchmarks of Bangla handwritten character classification show that the proposed model achieves better accuracy in classifying the data.
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