IBN-STR: A Robust Text Recognizer for Irregular Text in Natural Scenes

Li, Xiaoqian; Liu, Jie; Zhang, Guixuan; Zhang, Shuwu

doi:10.1109/icpr48806.2021.9412775

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Our motivation is to explore the valuable feature presentation from two aspects to improve the performance of the feature, which can reduce the accuracy difference between models on different test and training sets, as well as be able to improve the accuracy of models on multiple types of data sets. For the feature extract aspect, inspired by [9] that introduced [10] to extract robust features. We introduce a Representation Batch Normalization (RBN) to address the variability of feature representation among different instances and embed RBN into ResNet as the basic framework of the feature extractor.…”

Section: Introductionmentioning

confidence: 99%

“…We introduce a Representation Batch Normalization (RBN) to address the variability of feature representation among different instances and embed RBN into ResNet as the basic framework of the feature extractor. For the feature enhancement aspect, the U-shape network [9][10] can improve the recognition by merging multi-scale features. So, we propose a feature enhancement Network (FEN) to refine the feature representation, which contains low-level and high-level semantic information.…”

Section: Introductionmentioning

confidence: 99%

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Representative Batch Normalization for Scene Text Recognition

2022

KSII TIIS

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Scene text recognition has important application value and attracted the interest of plenty of researchers. At present, many methods have achieved good results, but most of the existing approaches attempt to improve the performance of scene text recognition from the image level. They have a good effect on reading regular scene texts. However, there are still many obstacles to recognizing text on low-quality images such as curved, occlusion, and blur. This exacerbates the difficulty of feature extraction because the image quality is uneven. In addition, the results of model testing are highly dependent on training data, so there is still room for improvement in scene text recognition methods. In this work, we present a natural scene text recognizer to improve the recognition performance from the feature level, which contains feature representation and feature enhancement. In terms of feature representation, we propose an efficient feature extractor combined with Representative Batch Normalization and ResNet. It reduces the dependence of the model on training data and improves the feature representation ability of different instances. In terms of feature enhancement, we use a feature enhancement network to expand the receptive field of feature maps, so that feature maps contain rich feature information. Enhanced feature representation capability helps to improve the recognition performance of the model. We conducted experiments on 7 benchmarks, which shows that this method is highly competitive in recognizing both regular and irregular texts. The method achieved top1 recognition accuracy on four benchmarks of IC03, IC13, IC15, and SVTP .

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Representative Batch Normalization for Scene Text Recognition

2022

KSII TIIS

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Scene text detection, recognition and language identification

Aji,

Mathew,

Patel

et al. 2023

SUSTAINABLE DEVELOPMENTS IN MATERIALS SCIENCE, TECHNOLOGY AND ENGINEERING: Sustainable Development in Material Science of Today

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SterAF: A Scene Text Recognizer with Appearance-Flow Rectification

Liao,

Du,

Liu

et al. 2024

Int. J. Patt. Recogn. Artif. Intell.

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As the demand for recognizing irregular text in natural scenes increases, people are increasingly realizing the value of such applications, such as license plate recognition systems, image search, handwriting recognition, and autonomous driving, which are profoundly changing our lives in the field of text recognition. Recent studies have shown that the recognition of curved text and perspective text has become an important challenge in the field of text recognition, and the correction of curved text is a key step to achieve accurate recognition. However, current methods use strained text image correction methods, resulting in poor recognition accuracy when recognizing curved text. Therefore, we propose an end-to-end framework called Scene Text Recognizer with Appearance-Flow rectification (SterAF), which includes a correction network and a recognition network. Specifically, the framework’s steps are as follows: first, the input text image is deformed through an appearance flow-based correction network to adaptively warp the text image, to prevent irregular and unnatural deformations of the text image. Second, a sequence-to-sequence recognition network predicts the sequence of characters in the corrected text image to accurately recognize the text in the image. Through subjective and objective experiments, our SterAF model has shown excellent performance in both qualitative and quantitative experiments.

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IBN-STR: A Robust Text Recognizer for Irregular Text in Natural Scenes

Cited by 3 publications

References 26 publications

Representative Batch Normalization for Scene Text Recognition

Representative Batch Normalization for Scene Text Recognition

Scene text detection, recognition and language identification

SterAF: A Scene Text Recognizer with Appearance-Flow Rectification

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