基于深度学习的对象检测方法已经在包括银行业在内的各个领域得到了应用,healthcare,电子治理,和学术界。近年来,从非结构化文档处理的不同场景或图像中进行文本检测和识别的研究工作受到了广泛的关注。本文的新颖之处在于详细讨论和实现了基于迁移学习的各种不同的印刷文本识别主干体系结构。在这篇研究文章中,作者比较了ResNet50、ResNet50V2、ResNet152V2、Inception、Xception,和VGG19骨干架构,具有预处理技术作为数据大小调整,归一化,以及标准OCRKaggle数据集上的噪声去除。Further,根据所达到的精度选择的前三个主干架构,然后执行超参数调谐以获得更准确的结果。Xception与ResNet相比表现良好,盗梦空间,VGG19,MobileNet架构通过实现高评估分数,准确性(98.90%)和最小损失(0.19)。根据该领域的现有研究,直到现在,在印刷或手写数据识别中使用的基于迁移学习的骨干体系结构在文献中没有得到很好的体现。我们将总的数据集分成80%用于训练,20%用于测试,然后分成不同的骨干架构模型,具有相同的纪元数,并发现Xception架构比其他架构实现了更高的准确度。此外,ResNet50V2模型的准确度(96.92%)高于ResNet152V2模型(96.34%).
Object detection methods based on deep learning have been used in a variety of sectors including banking, healthcare, e-governance, and academia. In recent years, there has been a lot of attention paid to research endeavors made towards text detection and recognition from different scenesor images of unstructured document processing. The article\'s novelty lies in the detailed discussion and implementation of the various transfer learning-based different backbone architectures for printed text recognition. In this research article, the authors compared the ResNet50, ResNet50V2, ResNet152V2, Inception, Xception, and
VGG19 backbone architectures with preprocessing techniques as data resizing, normalization, and noise removal on a standard OCR Kaggle dataset. Further, the top three backbone architectures selected based on the accuracy achieved and then hyper parameter tunning has been performed to achieve more accurate results. Xception performed well compared with the ResNet, Inception,
VGG19, MobileNet architectures by achieving high evaluation scores with accuracy (98.90%) and min loss (0.19). As per existing research in this domain, until now, transfer learning-based backbone architectures that have been used on printed or handwritten data recognition are not well represented in literature. We split the total dataset into 80 percent for training and 20 percent for testing purpose and then into different backbone architecture models with the same number of epochs, and found that the Xception architecture achieved higher accuracy than the others. In addition, the ResNet50V2 model gave us higher accuracy (96.92%) than the ResNet152V2 model (96.34%).