论文标题
孟加拉语手写数字识别使用CNN与可解释的AI
Bengali Handwritten Digit Recognition using CNN with Explainable AI
论文作者
论文摘要
手写角色识别是当今研究的热门话题。如果我们可以使用光学字符识别（OCR）技术将手写的纸转换为可搜索文本的文档，我们可以轻松理解内容，而无需阅读手写文档。英语中的OCR非常普遍，但是在孟加拉语中，很难找到高质量的OCR应用。如果我们可以将机器学习和深入学习与OCR合并，那可能是对这一领域的巨大贡献。各种研究人员提出了许多认可孟加拉语手写角色的策略。他们的工作中使用了许多ML算法和深层神经网络，但是对其模型的解释却没有。在我们的工作中，我们使用了各种机器学习算法和CNN来识别手写的孟加拉数字。我们从一些ML模型中获得了可接受的准确性，CNN为我们提供了出色的测试精度。 Grad-CAM在我们的CNN模型上用作XAI方法，该方法使我们对模型有所了解，并帮助我们检测了识别来自图像的数字的感兴趣的起源。
Handwritten character recognition is a hot topic for research nowadays. If we can convert a handwritten piece of paper into a text-searchable document using the Optical Character Recognition (OCR) technique, we can easily understand the content and do not need to read the handwritten document. OCR in the English language is very common, but in the Bengali language, it is very hard to find a good quality OCR application. If we can merge machine learning and deep learning with OCR, it could be a huge contribution to this field. Various researchers have proposed a number of strategies for recognizing Bengali handwritten characters. A lot of ML algorithms and deep neural networks were used in their work, but the explanations of their models are not available. In our work, we have used various machine learning algorithms and CNN to recognize handwritten Bengali digits. We have got acceptable accuracy from some ML models, and CNN has given us great testing accuracy. Grad-CAM was used as an XAI method on our CNN model, which gave us insights into the model and helped us detect the origin of interest for recognizing a digit from an image.
