Single-Character-Based Embedding Feature Aggregation Using Cross-Attention for Scene Text Super-Resolution
In textual vision scenarios, super-resolution aims to enhance textual quality and readability to facilitate downstream tasks. However, the ambiguity of character regions in complex backgrounds remains challenging to mitigate, particularly the interference between tightly connected characters. In thi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/7/2228 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849769329748344832 |
|---|---|
| author | Meng Wang Qianqian Li Haipeng Liu |
| author_facet | Meng Wang Qianqian Li Haipeng Liu |
| author_sort | Meng Wang |
| collection | DOAJ |
| description | In textual vision scenarios, super-resolution aims to enhance textual quality and readability to facilitate downstream tasks. However, the ambiguity of character regions in complex backgrounds remains challenging to mitigate, particularly the interference between tightly connected characters. In this paper, we propose single-character-based embedding feature aggregation using cross-attention for scene text super-resolution (SCE-STISR) to solve this problem. Firstly, a dynamic feature extraction mechanism is employed to adaptively capture shallow features by dynamically adjusting multi-scale feature weights based on spatial representations. During text–image interactions, a dual-level cross-attention mechanism is introduced to comprehensively aggregate the cropped single-character features with textual prior, also aligning semantic sequences and visual features. Finally, an adaptive normalized color correction operation is applied to mitigate color distortion caused by background interference. In TextZoom benchmarking, the text recognition accuracies of different recognizers are 53.6%, 60.9%, and 64.5%, which are improved by 0.9–1.4% over the baseline TATT, achieving an optimal SSIM value of 0.7951 and a PSNR of 21.84. Additionally, our approach improves accuracy by 0.2–2.2% over existing baselines on five text recognition datasets, validating the effectiveness of the model. |
| format | Article |
| id | doaj-art-91a42cc04b7c46ffab9f03e56f68d0bf |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-91a42cc04b7c46ffab9f03e56f68d0bf2025-08-20T03:03:27ZengMDPI AGSensors1424-82202025-04-01257222810.3390/s25072228Single-Character-Based Embedding Feature Aggregation Using Cross-Attention for Scene Text Super-ResolutionMeng Wang0Qianqian Li1Haipeng Liu2School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaSchool of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaSchool of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaIn textual vision scenarios, super-resolution aims to enhance textual quality and readability to facilitate downstream tasks. However, the ambiguity of character regions in complex backgrounds remains challenging to mitigate, particularly the interference between tightly connected characters. In this paper, we propose single-character-based embedding feature aggregation using cross-attention for scene text super-resolution (SCE-STISR) to solve this problem. Firstly, a dynamic feature extraction mechanism is employed to adaptively capture shallow features by dynamically adjusting multi-scale feature weights based on spatial representations. During text–image interactions, a dual-level cross-attention mechanism is introduced to comprehensively aggregate the cropped single-character features with textual prior, also aligning semantic sequences and visual features. Finally, an adaptive normalized color correction operation is applied to mitigate color distortion caused by background interference. In TextZoom benchmarking, the text recognition accuracies of different recognizers are 53.6%, 60.9%, and 64.5%, which are improved by 0.9–1.4% over the baseline TATT, achieving an optimal SSIM value of 0.7951 and a PSNR of 21.84. Additionally, our approach improves accuracy by 0.2–2.2% over existing baselines on five text recognition datasets, validating the effectiveness of the model.https://www.mdpi.com/1424-8220/25/7/2228scene text image super-resolutioncross-attentioncross-fertilizationtext recognition |
| spellingShingle | Meng Wang Qianqian Li Haipeng Liu Single-Character-Based Embedding Feature Aggregation Using Cross-Attention for Scene Text Super-Resolution Sensors scene text image super-resolution cross-attention cross-fertilization text recognition |
| title | Single-Character-Based Embedding Feature Aggregation Using Cross-Attention for Scene Text Super-Resolution |
| title_full | Single-Character-Based Embedding Feature Aggregation Using Cross-Attention for Scene Text Super-Resolution |
| title_fullStr | Single-Character-Based Embedding Feature Aggregation Using Cross-Attention for Scene Text Super-Resolution |
| title_full_unstemmed | Single-Character-Based Embedding Feature Aggregation Using Cross-Attention for Scene Text Super-Resolution |
| title_short | Single-Character-Based Embedding Feature Aggregation Using Cross-Attention for Scene Text Super-Resolution |
| title_sort | single character based embedding feature aggregation using cross attention for scene text super resolution |
| topic | scene text image super-resolution cross-attention cross-fertilization text recognition |
| url | https://www.mdpi.com/1424-8220/25/7/2228 |
| work_keys_str_mv | AT mengwang singlecharacterbasedembeddingfeatureaggregationusingcrossattentionforscenetextsuperresolution AT qianqianli singlecharacterbasedembeddingfeatureaggregationusingcrossattentionforscenetextsuperresolution AT haipengliu singlecharacterbasedembeddingfeatureaggregationusingcrossattentionforscenetextsuperresolution |