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IEEE Region 10 Conference (TENCON)

作者： Naosekpam, Veronica Shishir, Ailneni Sai Sahu, Nilkanta IIIT Guwahati Dept CSE Gauhati Assam India

ISBN: (纸本)9781665495325

text recognition from natural scene images is an arduous task due to non-horizontal shaped text caused by perspective distortion and low camera angle. This work presents a framework for natural scene text recognition that includes a text orientation correction module based on Inverse Compositional Spatial Transformer Network (IC-STN) [1]. The text recognition module is made up of an encoder-decoder attentional sequential network. The IC-STN is a differentiable module which can be trained using the error that is backpropagated by the text recognition network. The input image's orientation is corrected during testing using the IC-STN module that determines the parameters of image transformation techniques such as affine and homographic transformation. This, in turn, produces a more easily recognizable form of the text to be used by the sequence recognition network. As per our knowledge, this is the first time where IC-STN has been incorporated in the scene text recognition pipeline. Experimental analysis conducted on the benchmark datasets, ICDAR 2013 and CUTE80, proved that the proposed work performs better than the existing state-of-the-art text recognition systems.

关键词： scene text recognition Multi-oriented text Arbitrary shaped text IC-STN Encoder Decoder text Rectification

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Align, enhance and read: scene Tibetan text recognition with cross-sequence reasoning

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APPLIED SOFT COMPUTING 2025年 169卷

作者： Ke, Wenjun Liu, Yutian Yang, Xiaokang Wei, Jianguo Hou, Qingzhi Tianjin Univ Coll intelligence & Comp Tianjin 300000 Peoples R China Tianjin Univ State Key Lab Hydrwil Engn Intelligent Construct & Tianjin 300000 Peoples R China

Market demand has primarily driven scene text recognition to focus on widely spoken languages, such as English and Chinese. Current research put scarce attention on Tibetan, despite its substantial demand and potential applications. To fill this gap, we analyze the characteristics of Tibetan text and point out the corresponding difficulties in text recognition. Moreover, this study explores the performance degradation of existing recognition methods on Tibetan text, and proposes a flexible alignment decoding manner with crossmodal sequence reasoning for Tibetan text. Concretely, our work leverages the information between different modal sequences to learn detailed visual features for Tibetan characters with complex glyphs through crossmodal sequence reasoning. Furthermore, we design an innovative alignment decoding scheme that aligns characters in a dense manner, improving attention quality for the character alignment. Experimental results indicate that the performance of existing text recognition on Tibetan text is far behind that of English or Chinese text. With our design, the recognition on Tibetan text achieves significant improvement.

关键词： scene text recognition Tibetan text recognition Optical character recognition Deep learning

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scene text recognition with Image-text Matching-Guided Dictionary 17th

Scene Text Recognition with Image-Text Matching-Guided Dicti...

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17th International Conference on Document Analysis and recognition (ICDAR)

作者： Wei, Jiajun Zhan, Hongjian Tu, Xiao Lu, Yue Pal, Umapada East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China East China Normal Univ Chongqing Inst Chongqing 401120 Peoples R China Indian Stat Inst CVPR Unit Kolkata India

ISBN: (纸本)9783031417306;9783031417313

Employing a dictionary can efficiently rectify the deviation between the visual prediction and the ground truth in scene text recognition methods. However, the independence of the dictionary on the visual features may lead to incorrect rectification of accurate visual predictions. In this paper, we propose a new dictionary language model leveraging the scene Image-text Matching(SITM) network, which avoids the drawbacks of the explicit dictionary language model: 1) the independence of the visual features;2) noisy choice in candidates etc. The SITM network accomplishes this by using Image-text Contrastive (ITC) Learning to match an image with its corresponding text among candidates in the inference stage. ITC is widely used in vision-language learning to pull the positive image-text pair closer in feature space. Inspired by ITC, the SITM network combines the visual features and the text features of all candidates to identify the candidate with the minimum distance in the feature space. Our lexicon method achieves better results(93.8% accuracy) than the ordinary method results(92.1% accuracy) on six mainstream benchmarks. Additionally, we integrate our method with ABINet and establish new state-of-the-art results on several benchmarks.

关键词： Dictionary Language Model scene Image-text Matching Image-text Contrastive Learning scene text recognition

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scene text recognition USING PROGRESSIVE RECTIFICATION NETWORK AND SPELLING ERROR CORRECTION LANGUAGE MODEL 31

SCENE TEXT RECOGNITION USING PROGRESSIVE RECTIFICATION NETWO...

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2024 International Conference on Image Processing

作者： Peng, Ming-Zheng Cheng, Hao-Chung Le, Phuong-Thi Wang, Cheng-Chun Wang, Chien-Yao Wang, Jia-Ching Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan Taiwan Acad Sinica Taipei Taiwan

ISBN: (纸本)9798350349405;9798350349399

scene text recognition has gained popularity in deep neural network research. Compared to document text recognition, scene text recognition faces challenges such as complex backgrounds, diverse fonts, and blurred characters. Visual and semantic information must be considered in text recognition. While recent research has focused on improving semantic information, most studies have used English text datasets. Directly applying these methods to Chinese text datasets may not be effective. This work proposes a vision model with progressive rectification network and a Chinese scene text recognition method that uses a robust error-correcting language model to correct errors predicted by vision models. Firstly, the proposed progressive rectification network is of more effectiveness on the multi-oriented scene text images compared to present rectification method. On the other hand, the designed language model is used to correct errors predicted by vision models. The language model can handle low-quality images, including blurred, occluded, or nonsensical text. Experiments demonstrate that our method outperforms recent classic and state-of-the-art methods, making it a more powerful and suitable option for Chinese scene text recognition.

关键词： scene text recognition

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scene text recognition with Multi-decoders 21

Scene Text Recognition with Multi-decoders

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21st International Conference on Control, Automation and Systems (ICCAS)

作者： Wang, Yao Ha, Jong-Eun Seoul Natl Univ Sci & Technol Grad Sch Automot Engn Seoul 13391 South Korea Seoul Nation Univ Sci & Technol Dept Mech & Automot Engn Seoul 13391 South Korea

ISBN: (纸本)9788993215212

In this article, we focus on the scene text recognition problem, which is one of the challenging sub-files of computer vision because of the random existence of scene text. Recently, scene text recognition has achieved state-of-art performance because of the improvement of deep learning. At present, encoder-decoder architecture was widely used for scene recognition tasks, which consist of feature extractor, sequence module. Specifically, at the decoder part, connectionist temporal classification(CTC), attention mechanism, and transformer(self-attention) are three main approaches used in recent research. CTC decoder is flexible and can handle sequences with large changes in length for its align sequences features with labels in a frame-wise manner. Attention decoder can learn better and deeper feature expression and get the better position information of each character. Attention decoder can get more robust and accurate performance for both regular and irregular scene text. Moreover, a novel decoder mechanism is introduced in our study. The proposed architecture has several advantages: the model can be trained using the end-to-end manner under the condition of multi decoders, and can deal with the sequences of arbitrary length and the images of arbitrary shape. Extensive experiments on standard benchmarks demonstrate that our model's performance is improved for regular and irregular text recognition.

关键词： scene text recognition End to end frame CTC decoder module Attention decoder module

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scene text recognition VIA GATED CASCADE ATTENTION

SCENE TEXT RECOGNITION VIA GATED CASCADE ATTENTION

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wang, Siwei Wang, Yongtao Qin, Xiaoran Zhao, Qijie Tang, Zhi Peking Univ Inst Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781538695524

scene text recognition is very challenging due to the complex background, low resolution, perspective distortion and curved placement, etc. Most of the state-of-the-art methods adopt the attention-based encoder-decoder framework, and usually get suboptimal recognition performance for challenging text images due to the misalignment between attention region and target character region. In this paper, a novel module, named Gated Cascade Attention Module (GCAM), is proposed to increase the alignment precision of attention in a cascade way. Moreover, a channel and spatial attention module is introduced into the encoder to extract more discriminative features for text recognition. By assembling these two modules, a novel scene text recognizer is developed, and extensive experiments demonstrate it can achieve state-of-the-art results on multiple benchmarks of regular and irregular text images.

关键词： scene text recognition Cascade Attention Channel and Spatial Attention

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scene text recognition with Single-Point Decoding Network 2nd

Scene Text Recognition with Single-Point Decoding Network

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2nd CAAI International Conference on Artificial Intelligence (CICAI)

作者： Chen, Lei Qin, Haibo Zhang, Shi-Xue Yang, Chun Yin, Xucheng Univ Sci & Technol Beijing Beijing Peoples R China

ISBN: (纸本)9783031204968;9783031204975

In recent years, attention-based scene text recognition methods have been very popular and attracted the interest of many researchers. Attention-based methods can adaptively focus attention on a small area or even single point during decoding, in which the attention matrix is nearly one-hot distribution. Furthermore, the whole feature maps will be weighted and summed by all attention matrices during inference, causing huge redundant computations. In this paper, we propose an efficient attention-free Single-Point Decoding Network (dubbed SPDN) for scene text recognition, which can replace the traditional attention-based decoding network. Specifically, we propose Single-Point Sampling Module (SPSM) to efficiently sample one key point on the feature map for decoding one character. In this way, our method can not only precisely locate the key point of each character but also remove redundant computations. Based on SPSM, we design an efficient and novel single-point decoding network to replace the attention-based decoding network. Extensive experiments on publicly available benchmarks verify that our SPDN can greatly improve decoding efficiency without sacrificing performance.

关键词： Single-point Attention scene text recognition

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scene text recognition with Auto-Aligned Feature Generator 19

Scene Text Recognition with Auto-Aligned Feature Generator

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19th IEEE International Conference on Data Mining (ICDM)

作者： Yang, Qiangpeng Jin, Hongsheng Cheng, Mengli Zhou, Wenmeng Huang, Jun Lin, Wei Alibab Grp Hangzhou Peoples R China

ISBN: (纸本)9781728146034

scene text recognition has attracted increasing attention in computer vision due to its various applications. Most of the existing scene text recognition methods are under the encoder-decoder framework. In order to improve text feature learning of these methods, Generative Adversarial Networks (GANs) are recently integrated to generate clean text images without distorted letters. However, the existing GANs assume the input images are spatially aligned, while the words in natural images are often in irregular shapes. The misalignment brings a big problem for both image generation and text recognition. In this paper, we present a novel text feature alignment network to solve this problem. Our method can handle both horizontal and vertical images with irregular texts. Our proposed framework is end-to-end trainable, and extensive experiments on several public benchmarks demonstrate its superiority in terms of both effectiveness and efficiency.

关键词： Feature Alignment Image Generator scene text recognition

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scene text recognition IN MULTIPLE FRAMES BASED ON text TRACKING

SCENE TEXT RECOGNITION IN MULTIPLE FRAMES BASED ON TEXT TRAC...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Rong, Xuejian Yi, Chucai Yang, Xiaodong Tian, Yingli CUNY City Coll New York NY 10031 USA CUNY Grad Ctr New York NY USA

ISBN: (纸本)9781479947614

text signage as visual indicators in natural scene plays an important role in navigation and notification in our daily life. Most previous methods of scene text extraction are developed from a single scene image. In this paper, we propose a multi-frame based scene text recognition method by tracking text regions in a video captured by a moving camera. The main contributions of this paper are as follows. First, we present a framework of scene text recognition in multiple frames based on feature representation of scene text character (STC) for character prediction and conditional random field (CRF) model for word configuration. Second, a feature representation of STC is employed from dense sampled SIFT descriptors and Fisher Vector. Third, we collect a dataset for text information extraction from natural scene videos. Our proposed multi-frame scene text recognition is more compatible with image/video-based mobile applications. The experimental results demonstrate that STC prediction and word configuration in multiple frames based on text tracking significantly improves the performance of scene text recognition.

关键词： scene text recognition text tracking feature representation of scene text character Fisher Vector video dataset of scene text

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scene text recognition with Cascade Attention Network 21

Scene Text Recognition with Cascade Attention Network

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11th International Conference on Multimedia Retrieval (ICMR)

作者： Zhang, Min Ma, Meng Wang, Ping Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peking Univ Natl Engn Res Ctr Software Engn Beijing Peoples R China Minist Educ Key Lab High Confidence Software Technol PKU Beijing Peoples R China

ISBN: (纸本)9781450384636

scene text recognition (STR) has experienced increasing popularity both in academia and in industry. Regarding STR as a sequence prediction task, most state-of-the-art (SOTA) approaches employ the attention-based encoder-decoder architecture to recognize texts. However, these methods still struggle in localizing the precise alignment center associated with the current character, which is also named as the attention drift phenomenon. One major reason is that directly converting low-quality or distorted word images to sequential features may introduce confusing information and thus mislead the network. To address the problem, this paper proposes a cascade attention network. The model is composed of three novel attention modules: a vanilla attention module that attends to sequential features from the horizontal direction, a cross-network attention module to take advantage of both one-dimension contextual information and two-dimension visual distributions, and an aspects fusion attention module to fuse spatial and channel-wise information. Accordingly, the network manages to yield distinguished and refined representations correlated to the target sequence. Compared to SOTA methods, experimental results on seven benchmarks demonstrate the superiority of our framework in recognizing scene texts on various conditions.

关键词： scene text recognition Cascade attention network Attention drift

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