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Comments (12)

DayBreak-u avatar DayBreak-u commented on May 8, 2024 1

你自己测过了吗,我感觉效果不是特别好 哈哈

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DayBreak-u avatar DayBreak-u commented on May 8, 2024 1

+1

crnn 横排 我主要是https://github.com/YCG09/chinese_ocr 提到的数据集和自己的一些生成的数据集,竖排我主要是用生成的数据集

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DayBreak-u avatar DayBreak-u commented on May 8, 2024 1

识别结果展示的图片是在训练集里的图片吗?
这个艺术字都能识别这么好,效果很赞啊 ,这个样本怎么造?

没有在训练集的,我是基于这个项目https://github.com/Sanster/text_rendererhttps://github.com/yanhaiming56/SynthText_Chinese_py3生成的,当然自己也收集了许多数据集。 效果其实不太好,你可以自己试试其他图片

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wangbingok1118 avatar wangbingok1118 commented on May 8, 2024

楼主好,我看您的模型效果很好,请问是用的什么数据集训练的?

+1

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piaobuliao avatar piaobuliao commented on May 8, 2024

识别结果展示的图片是在训练集里的图片吗?
这个艺术字都能识别这么好,效果很赞啊 ,这个样本怎么造?

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left4back avatar left4back commented on May 8, 2024

首先感谢您的模型分享
我想问问您是用什么方法进行数据生成的呢,我之前也试过用生成的数据做票据识别,但即使肉眼看上去很相似的数据,最后训练出来的识别模型效果依然很差

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DayBreak-u avatar DayBreak-u commented on May 8, 2024

首先感谢您的模型分享
我想问问您是用什么方法进行数据生成的呢,我之前也试过用生成的数据做票据识别,但即使肉眼看上去很相似的数据,最后训练出来的识别模型效果依然很差

用一部分真实的数据集呢

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left4back avatar left4back commented on May 8, 2024

首先感谢您的模型分享
我想问问您是用什么方法进行数据生成的呢,我之前也试过用生成的数据做票据识别,但即使肉眼看上去很相似的数据,最后训练出来的识别模型效果依然很差

用一部分真实的数据集呢

您好,可否大概透露一下生成数据与真实数据的比例呢,我猜应该是用生成的数据训练再用真实数据finetune,但汉字那么多用少量数据finetune(比如说真实数据甚至不能覆盖字典中的所有字)会有提升吗

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learn01one avatar learn01one commented on May 8, 2024

楼主你好,你是对竖排和横排混合着训练的吗,还是有其它训练策略,可否提供一些思路,谢谢

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DayBreak-u avatar DayBreak-u commented on May 8, 2024

楼主你好,你是对竖排和横排混合着训练的吗,还是有其它训练策略,可否提供一些思路,谢谢

你好,竖排和横排我是分开训练的,竖排主要是用90度的字体进行生成的样本

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yangsuhui avatar yangsuhui commented on May 8, 2024

楼主,您好,请问竖排是用90度的字体进行生成的样本怎么理解?
是用https://github.com/Sanster/text_renderer或者https://github.com/yanhaiming56/SynthText_Chinese_py3项目生成的吗?
但是这两个项目生成的是水平文本,怎么设置字体是90°的?
谢谢

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DayBreak-u avatar DayBreak-u commented on May 8, 2024

楼主,您好,请问竖排是用90度的字体进行生成的样本怎么理解?
是用https://github.com/Sanster/text_renderer或者https://github.com/yanhaiming56/SynthText_Chinese_py3项目生成的吗?
但是这两个项目生成的是水平文本,怎么设置字体是90°的?
谢谢

在readme已经说明了 也提供了相应字体在https://github.com/ouyanghuiyu/chineseocr_lite/tree/master/vertical_text_fonts/fonts

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