基于CRNN模型的中文场景文字识别

ChineseScene Text Recognition based on CRNN model

中文场景文字识别(STR)是光学字符识别(OCR)技术的重要研究方向,在拍照翻译、无人驾驶等领域广泛应用。但是,中文场景下的文字面临着字体和字符种类多、文字背景复杂等问题。本文着眼于“中国街景”图像,基于CRNN模型提出了一种免分割、端到端的中文场景文字识别方法。首先CNN提取图像卷积特征,然后RNN进行序列特征预测,其中Bi-GRU有效抑制梯度消失或梯度爆炸,Dropout可以防止过拟合,最后引入CTC作为损失函数解决训练时字符无法对齐的问题。本文用Python实现了算法,以较好的效果完成了实验。

Chinese scene character recognition(STR)is an important research direction of optical character recognition(OCR)technology,which is widely used in the fields of photo translation and unmanned driving.However,the characters in Chinese scene are faced with many problems,such as many types of fonts and characters,complex text background and so on.This paper focuses on the"Chinese street view"image,and proposes a segmentation free,end-to-end Chinese scene text recognition method based on crnn model.Firstly,CNN extracts image convolution features,and then RNN performs sequence feature prediction.Bi Gru can effectively suppress gradient disappearance or gradient explosion,dropout can prevent over fitting.Finally,CTC is introduced as a loss function to solve the problem that characters cannot be aligned during training.In this paper,Python is used to implement the algorithm,and the experiment is completed with good effect.