篇名 |
Document Classification Using Lightweight Neural Network
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並列篇名 | Document Classification Using Lightweight Neural Network |
作者 | Chung-Hsing Chen、Ko-Wei Huang |
英文摘要 | In recent years, OCR data has been used for learning and analyzing document classification. In addition, some neural networks have used image recognition for training, such as the network published by the ImageNet Large Scale Visual Recognition Challenge for document image training, AlexNet, GoogleNet, and MobileNet. Document image classification is important in data extraction processes and often requires significant computing power. Furthermore, it is difficult to implement image classification using general computers without a graphics processing unit (GPU). Therefore, this study proposes a lightweight neural network application that can perform document image classification on general computers or the Internet of Things (IoT) without a GPU. Plustek Inc. provided 3065 receipts belonging to 58 categories. Three datasets were considered as test samples while the remaining were considered as training samples to train the network to obtain a classifier. After the experiments, the classifier achieved 98.26% accuracy, and only 3 out of 174 samples showed errors.
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起訖頁 | 1505-1511 |
關鍵詞 | Documents classification、CNN、IoT、Deep learning、Edge computing |
刊名 | 網際網路技術學刊 |
期數 | 202312 (24:7期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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QR Code | |
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| A Dynamic Model for the Computation of Gesture Types for Image-based Software Agents |