Accurate, data-efficient, unconstrained text recognition with convolutional neural networks

作者：

Highlights：

• We propose a novel neural network architecture for unconstrained text recognition.

• Our proposed architecture uses only the highly efficient convolutional primitives

• We achieve state-of-the-art results on seven public benchmark datasets.

• Our proposed model has won the ICFHR2018 Competition on Automated Text Recognition.

摘要

•We propose a novel neural network architecture for unconstrained text recognition.•Our proposed architecture uses only the highly efficient convolutional primitives•We achieve state-of-the-art results on seven public benchmark datasets.•Our proposed model has won the ICFHR2018 Competition on Automated Text Recognition.

论文关键词：Text recognition,Optical character recognition,Handwriting recognition,CAPTCHA Solving,License plate recognition,Convolutional neural network,Deep learning

论文评审过程：Received 14 August 2019, Revised 22 May 2020, Accepted 31 May 2020, Available online 1 July 2020, Version of Record 6 August 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107482