Natural scene text identification method based on convolution attention network

A natural scene and text recognition technology, applied in the field of natural scene text recognition based on convolutional attention network, can solve the problem of high algorithm complexity, and achieve the effect of improving abstract expression, reducing algorithm complexity and improving accuracy.

Active Publication Date: 2018-10-02
UNIV OF SCI & TECH OF CHINA
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For a sequence of length n, using RNN to model character sequences requires O(n) operati

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  • Natural scene text identification method based on convolution attention network
  • Natural scene text identification method based on convolution attention network
  • Natural scene text identification method based on convolution attention network

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Embodiment Construction

[0016] The technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0017] Embodiments of the present invention provide a method for recognizing text in natural scenes based on a convolutional attention network, which is based on an encoder-decoder structure, but uses a fully convolutional method to identify text images in natural scenes. That is, both the encoder and the decoder of this method are composed of a convolutional neural network (CNN), and CNN is used to replace RNN to decode image features to recognize char...

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Abstract

The invention discloses a natural scene text identification method based on a convolution attention network; the method comprises the following steps: using a two-dimensional convolution CNN as an encoder, extracting High-level semantic features of an input image, and outputting a corresponding feature graph to a decoder; using a one-dimensional convolution CNN as the decoder, combining an attention mechanism to integrate the High-level semantic feature formed by the encoder with a character level language model, thus forming a decode character sequence corresponding to the input image. For asequence with the length of n, the method uses the CNN modeling character sequence with a convolution kernel as s, and only O (n/s) times of operation is needed to obtain the long term dependence expression, thus greatly reducing the algorithm complexity; in addition, because of convolution operation characteristics, the CNN can be better paralleled when compared with a RNN, thus performing advantages of GPU resources; more importantly, a deep model can be obtained via a convolution layer stacking mode, thus improving abstractness expression of a higher level, and improving the model accuracy.

Description

technical field [0001] The invention relates to the field of text recognition in natural scene images, in particular to a method for recognizing text in natural scenes based on a convolutional attention network. Background technique [0002] With the increasing popularity of terminal devices such as mobile phones and tablets, it is becoming more and more important to recognize and understand images taken in natural scenes containing text. Due to factors such as image quality, complex background, and noise interference, text recognition in natural scenes faces great challenges. A complete end-to-end natural scene text recognition generally includes two stages: the text detection stage and the text recognition stage. The text detection stage locates the location of the text from the whole image, and the text recognition stage converts the image blocks containing only text into text strings. [0003] At present, due to the good language modeling ability of Recurrent Neural Ne...

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Application Information

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IPC IPC(8): G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/63G06V30/10G06N3/045G06F18/2413
Inventor 谢洪涛张勇东
Owner UNIV OF SCI & TECH OF CHINA
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