CSTR: A Classification Perspective on Scene Text Recognition

• URL: http://arxiv.org/abs/2102.10884v1
• Date: Mon, 22 Feb 2021 10:32:07 GMT
• Title: CSTR: A Classification Perspective on Scene Text Recognition
• Authors: Hongxiang Cai, Jun Sun, Yichao Xiong
• Abstract summary: We propose a new perspective on scene text recognition, in which we model the scene text recognition as an image classification problem. Based on the image classification perspective, a scene text recognition model is proposed, which is named as CSTR. CSTR achieves nearly state-of-the-art performance on six public benchmarks including regular text, irregular text.
• Score: 3.286661798699067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The prevalent perspectives of scene text recognition are from sequence to sequence (seq2seq) and segmentation. In this paper, we propose a new perspective on scene text recognition, in which we model the scene text recognition as an image classification problem. Based on the image classification perspective, a scene text recognition model is proposed, which is named as CSTR. The CSTR model consists of a series of convolutional layers and a global average pooling layer at the end, followed by independent multi-class classification heads, each of which predicts the corresponding character of the word sequence in input image. The CSTR model is easy to train using parallel cross entropy losses. CSTR is as simple as image classification models like ResNet \cite{he2016deep} which makes it easy to implement, and the fully convolutional neural network architecture makes it efficient to train and deploy. We demonstrate the effectiveness of the classification perspective on scene text recognition with thorough experiments. Futhermore, CSTR achieves nearly state-of-the-art performance on six public benchmarks including regular text, irregular text. The code will be available at https://github.com/Media-Smart/vedastr.

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