Semantic Segmentation of PHT Based on Improved DeeplabV3

This work aimed to address the two shortcomings of the printed and handwritten texts (PHT) classification. The classification accuracy of FCN and U-net, which are used for PHT pixel-level classification, still has room to improve. PHT public datasets have small sample sizes, and the generalization a...

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Bibliographic Details
Published inMathematical problems in engineering Vol. 2022; pp. 1 - 8
Main Author Fang, Haiquan
Format Journal Article
LanguageEnglish
Published New York Hindawi 19.03.2022
Hindawi Limited
Subjects
Accuracy
Algorithms
Artificial intelligence
Classification
Datasets
Deep learning
Digitization
Discriminant analysis
Handwriting
Labeling
Mathematical problems
Pixels
Semantic segmentation
Semantics
Teaching methods
Texts
Wavelet transforms
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Summary:This work aimed to address the two shortcomings of the printed and handwritten texts (PHT) classification. The classification accuracy of FCN and U-net, which are used for PHT pixel-level classification, still has room to improve. PHT public datasets have small sample sizes, and the generalization ability of the models is not good. In this paper, first, a pixel-level sample-making method for PHT identification was proposed, and a PHT dataset 2021 (PHTD 2021), containing 3,000 samples, was constructed. Second, because there is a large number of words but the contours are small in documents, the DeeplabV3+ model was improved. The network layer number and pooling times were reduced, and the convolution kernel and dilated rate were increased. In the experiment, the improved DeeplabV3+ model had a classification accuracy of 95.06% on the test samples from the PHTD 2021 dataset. The improved DeeplabV3+ model has a higher recognition accuracy than the FCN and DeeplabV3+ models. Finally, after the classification of PHT, applications of handwritten texts removal and handwritten texts extraction are provided.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/6228532