Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing | Paper Citation Record

Observation fccc01d2-dca8-49ed-acf3-b0a722b498c4 · outbound

This paper cites What if we only use real datasets for scene text recognition? toward scene text recognition with fewer labels.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing What if we only use real datasets for scene text recognition? toward scene text recognition with fewer labels

Reference 1

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Observation da53949c-5758-44a6-98a4-9ca3779e9c51 · outbound

This paper cites Scene text recognition with permuted autoregressive sequence models.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Scene text recognition with permuted autoregressive sequence models

Reference 2

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Observation fcbbbe8d-66ca-4b9b-a966-3291804a1572 · outbound

This paper cites Combining labeled and unlabeled data with co-training.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Combining labeled and unlabeled data with co-training

Reference 3

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Observation 420b2ee8-cfab-4afd-9c35-c2964e1bfb29 · outbound

This paper cites Big self-supervised models are strong semi-supervised learners.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Big self-supervised models are strong semi-supervised learners

Reference 4

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Observation 9018dbcf-4060-42c6-aeec-61f76ba69e31 · outbound

This paper cites Lister: Neighbor decoding for length-insensitive scene text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Lister: Neighbor decoding for length-insensitive scene text recognition

Reference 5

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Observation 24264dbc-b9aa-4b1f-a314-30913334908c · outbound

This paper cites Icdar2019 robust reading challenge on arbitrary-shaped text-rrc-art.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Icdar2019 robust reading challenge on arbitrary-shaped text-rrc-art

Reference 6

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Observation 5051a996-67d7-44fc-8f63-82a8a7b2effc · outbound

This paper cites Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

Reference 7

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Observation e209a1b1-5d9d-4f31-8bbf-3b8d66283c13 · outbound

This paper cites Tri-net for semi-supervised deep learning.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Tri-net for semi-supervised deep learning

Reference 8

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Observation b5a86e51-3004-430d-b34f-39f3734f5fec · outbound

This paper cites SVTR: Scene Text Recognition with a Single Visual Model.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing SVTR: Scene Text Recognition with a Single Visual Model

Reference 9

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Observation 1c3eeeb2-f42d-4984-962b-06bebc418b7d · outbound

This paper cites Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition

Reference 10

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Observation adb0eab4-3913-46be-bd01-a278af346013 · outbound

This paper cites Semi-supervised scene text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Semi-supervised scene text recognition

Reference 11

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Observation 39f2bcdc-88bb-4061-ad43-741a477d0cef · outbound

This paper cites Synthetic data for text localisation in natural images.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Synthetic data for text localisation in natural images

Reference 12

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Observation 5c5694ea-cf91-46f7-9696-f6b9cf827f8d · outbound

This paper cites Judging a Book By its Cover.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Judging a Book By its Cover

Reference 13

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Observation 03a3ee34-6395-4e34-b9ca-e0e9949253fe · outbound

This paper cites Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition

Reference 14

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This paper cites Revisiting scene text recognition: A data perspective.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Revisiting scene text recognition: A data perspective

Reference 15

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Observation 9122b8b6-39d0-4caf-8ca0-9d47f603df8e · outbound

This paper cites Icdar 2015 competition on robust reading.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Icdar 2015 competition on robust reading

Reference 16

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Observation 7a3670e4-c944-4441-b903-6b0d6b83edbc · outbound

This paper cites Icdar 2013 robust reading competition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Icdar 2013 robust reading competition

Reference 17

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Observation 5f9cb87e-51f3-4e08-bf94-8b93d56c0e38 · outbound

This paper cites The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale

Reference 18

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Observation 3b2126b1-f7d0-434c-b1fd-00f40a7f0dc2 · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Temporal Ensembling for Semi-Supervised Learning

Reference 19

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This paper cites Recognizing handwritten mathematical expressions via paired dual loss attention network and printed mathematical expressions.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Recognizing handwritten mathematical expressions via paired dual loss attention network and printed mathematical expressions

Reference 20

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This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

Reference 21

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This paper cites Char-net: A character-aware neural network for distorted scene text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Char-net: A character-aware neural network for distorted scene text recognition

Reference 22

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Observation 580065dd-282e-4161-a037-67e6959ff36b · outbound

This paper cites Moran: A multi-object rectified attention network for scene text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Moran: A multi-object rectified attention network for scene text recognition

Reference 23

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Observation 3f768323-78bb-46c2-8f3c-929b69736e6f · outbound

This paper cites Scene text recognition using higher order language priors.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Scene text recognition using higher order language priors

Reference 24

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This paper cites Multi-modal text recognition networks: Interactive enhancements between visual and semantic features.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Multi-modal text recognition networks: Interactive enhancements between visual and semantic features

Reference 25

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Observation eb974cdf-4cee-4abe-8a26-0bc492eebe45 · outbound

This paper cites Semi-Supervised Learning with Generative Adversarial Networks.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Semi-Supervised Learning with Generative Adversarial Networks

Reference 26

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This paper cites Seq-ups: Sequential uncertainty-aware pseudo- label selection for semi-supervised text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Seq-ups: Sequential uncertainty-aware pseudo- label selection for semi-supervised text recognition

Reference 27

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This paper cites Recognizing text with perspective distortion in natural scenes.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Recognizing text with perspective distortion in natural scenes

Reference 28

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This paper cites Semi- supervised learning with ladder networks.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Semi- supervised learning with ladder networks

Reference 29

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This paper cites A robust arbitrary text detection system for natural scene images.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing A robust arbitrary text detection system for natural scene images

Reference 30

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This paper cites Regularization with stochastic transfor- mations and perturbations for deep semi-supervised learning.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Regularization with stochastic transfor- mations and perturbations for deep semi-supervised learning

Reference 31

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This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 32

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This paper cites An end-to-end trainable neural network for image- based sequence recognition and its application to scene text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing An end-to-end trainable neural network for image- based sequence recognition and its application to scene text recognition

Reference 33

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This paper cites Aster: An attentional scene text recognizer with flexible rectification.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Aster: An attentional scene text recognizer with flexible rectification

Reference 34

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Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Super-convergence: Very fast training of neural networks using large learning rates

Reference 35

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Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

Reference 36

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This paper cites Mean teachers are better role models: Weight-averaged con- sistency targets improve semi-supervised deep learning results.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Mean teachers are better role models: Weight-averaged con- sistency targets improve semi-supervised deep learning results

Reference 37

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This paper cites Visualizing data using t-sne.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Visualizing data using t-sne

Reference 38

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This paper cites COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images

Reference 39

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This paper cites End-to-end scene text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing End-to-end scene text recognition

Reference 40

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This paper cites Multi-granularity prediction for scene text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Multi-granularity prediction for scene text recognition

Reference 41

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This paper cites From two to one: A new scene text recognizer with visual language modeling network.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing From two to one: A new scene text recognizer with visual language modeling network

Reference 42

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This paper cites Symmetrical linguistic feature distillation with clip for scene text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Symmetrical linguistic feature distillation with clip for scene text recognition

Reference 43

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This paper cites Handwritten mathematical expression recognition via paired adversarial learning.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Handwritten mathematical expression recognition via paired adversarial learning

Reference 44

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This paper cites Toward understanding wordart: Corner-guided transformer for scene text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Toward understanding wordart: Corner-guided transformer for scene text recognition

Reference 45

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Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Symmetry-constrained rectification network for scene text recognition

Reference 46

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This paper cites Sequential visual and semantic consistency for semi-supervised text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Sequential visual and semantic consistency for semi-supervised text recognition

Reference 47

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This paper cites A survey on deep semi-supervised learning.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing A survey on deep semi-supervised learning

Reference 48

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This paper cites Towards accurate scene text recognition with semantic reasoning networks.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Towards accurate scene text recognition with semantic reasoning networks

Reference 49

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This paper cites Robustscanner: Dynamically enhancing positional clues for robust text recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Robustscanner: Dynamically enhancing positional clues for robust text recognition

Reference 50

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This paper cites Esir: End-to-end scene text recognition via iterative image rectification.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Esir: End-to-end scene text recognition via iterative image rectification

Reference 51

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This paper cites Linguistic More: Taking a Further Step toward Efficient and Accurate Scene Text Recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Linguistic More: Taking a Further Step toward Efficient and Accurate Scene Text Recognition

Reference 52

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This paper cites Uber- text: A large-scale dataset for optical character recognition from street-level imagery.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Uber- text: A large-scale dataset for optical character recognition from street-level imagery

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:31.347034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3985beaf-ff34-4c3c-8bf9-74be9633cd0c · outbound

This paper cites Pushing the performance limit of scene text recognizer without human annotation.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing Pushing the performance limit of scene text recognizer without human annotation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:16:31.322484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aa199e46-5e21-427e-9a33-207111af1b73 · outbound

This paper cites TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition.

Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:16:31.073853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Pith citing papers