@inproceedings{he-etal-2023-rong,
title = "融合多粒度特征的缅甸语文本图像识别方法({B}urmese Language Recognition Method Fused with Multi-Granularity Features)",
author = "He, Enyu and
Chen, Rui and
Mao, Cunli and
Huang, Yuxin and
Gao, Shengxaing and
Yu, Zhengtao",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-1.11",
pages = "124--134",
abstract = "{``}缅甸语属于东南亚低资源语言,缅甸语文本图像识别对开展缅甸语机器翻译等任务具有重要意义。由于缅甸语属于典型的字符组合型语言,一个感受野内存在多个字符嵌套,现有缅甸语识别方法主要是从字符粒度进行识别,在解码时会出现某些字符未能正确识别而导致局部乱码。考虑到缅甸语存在特殊的字符组合规则,本文提出了一种融合多粒度特征的缅甸语文本图像识别方法,将较细粒度的字符粒度和较粗粒度的字符簇粒度进行序列建模,然后将两种粒度特征序列进行融合后利用解码器进行解码。实验结果表明,该方法能够有效缓解识别结果乱码的现象,并且在人工构建的数据集上相比{``}VGG16+BiLSTM+Transformer{''}的基线模型识别准确率提高2.4{\%},达到97.35{\%}。 ''",
language = "Chinese",
}
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<abstract>“缅甸语属于东南亚低资源语言,缅甸语文本图像识别对开展缅甸语机器翻译等任务具有重要意义。由于缅甸语属于典型的字符组合型语言,一个感受野内存在多个字符嵌套,现有缅甸语识别方法主要是从字符粒度进行识别,在解码时会出现某些字符未能正确识别而导致局部乱码。考虑到缅甸语存在特殊的字符组合规则,本文提出了一种融合多粒度特征的缅甸语文本图像识别方法,将较细粒度的字符粒度和较粗粒度的字符簇粒度进行序列建模,然后将两种粒度特征序列进行融合后利用解码器进行解码。实验结果表明,该方法能够有效缓解识别结果乱码的现象,并且在人工构建的数据集上相比“VGG16+BiLSTM+Transformer”的基线模型识别准确率提高2.4%,达到97.35%。 ”</abstract>
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%0 Conference Proceedings
%T 融合多粒度特征的缅甸语文本图像识别方法(Burmese Language Recognition Method Fused with Multi-Granularity Features)
%A He, Enyu
%A Chen, Rui
%A Mao, Cunli
%A Huang, Yuxin
%A Gao, Shengxaing
%A Yu, Zhengtao
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G Chinese
%F he-etal-2023-rong
%X “缅甸语属于东南亚低资源语言,缅甸语文本图像识别对开展缅甸语机器翻译等任务具有重要意义。由于缅甸语属于典型的字符组合型语言,一个感受野内存在多个字符嵌套,现有缅甸语识别方法主要是从字符粒度进行识别,在解码时会出现某些字符未能正确识别而导致局部乱码。考虑到缅甸语存在特殊的字符组合规则,本文提出了一种融合多粒度特征的缅甸语文本图像识别方法,将较细粒度的字符粒度和较粗粒度的字符簇粒度进行序列建模,然后将两种粒度特征序列进行融合后利用解码器进行解码。实验结果表明,该方法能够有效缓解识别结果乱码的现象,并且在人工构建的数据集上相比“VGG16+BiLSTM+Transformer”的基线模型识别准确率提高2.4%,达到97.35%。 ”
%U https://aclanthology.org/2023.ccl-1.11
%P 124-134
Markdown (Informal)
[融合多粒度特征的缅甸语文本图像识别方法(Burmese Language Recognition Method Fused with Multi-Granularity Features)](https://aclanthology.org/2023.ccl-1.11) (He et al., CCL 2023)
ACL