Papers, Datasets, Algorithms, SOTA for STR. Long-time Maintaining
SOTA • Papers • Datasets • Code • Our Framework
Revisiting Scene Text Recognition: A Data Perspective
Dataset | Description | Examples | BaiduNetdisk link |
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SynthText | 9 million synthetic text instance images from a set of 90k common English words. Words are rendered onto nartural images with random transformations | Scene text datasets(提取码:emco) | |
MJSynth | 6 million synthetic text instances. It's a generation of SynthText. | Scene text datasets(提取码:emco) |
Dataset | Description | Examples | BaiduNetdisk link |
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IIIT5k-Words(IIIT5K) | 3000 test images instances. Take from street scenes and from originally-digital images | Scene text datasets(提取码:emco) | |
Street View Text(SVT) | 647 test images instances. Some images are severely corrupted by noise, blur, and low resolution | Scene text datasets(提取码:emco) | |
StreetViewText-Perspective(SVT-P) | 639 test images instances. It is specifically designed to evaluate perspective distorted textrecognition. It is built based on the original SVT dataset by selecting the images at the sameaddress on Google Street View but with different view angles. Therefore, most text instancesare heavily distorted by the non-frontal view angle. | Scene text datasets(提取码:emco) | |
ICDAR 2003(IC03) | 867 test image instances | Scene text datasets(提取码:mfir) | |
ICDAR 2013(IC13) | 1015 test images instances | Scene text datasets(提取码:emco) | |
ICDAR 2015(IC15) | 2077 test images instances. As text images were taken by Google Glasses without ensuringthe image quality, most of the text is very small, blurred, and multi-oriented | Scene text datasets(提取码:emco) | |
CUTE80(CUTE) | 288 It focuses on curved text recognition. Most images in CUTE have acomplex background, perspective distortion, and poor resolution | Scene text datasets(提取码:emco) |
Dataset | Description | Examples | BaiduNetdisk link |
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COCO-Text | 39K Created from the MS COCO dataset. As the MS COCO dataset is not intended to capture text. COCO contains many occluded or low-resolution texts | Others(提取码:DLVC) | |
RCTW | 8186 in English. RCTW is created for Reading Chinese Text in the Wild competition. We select those in english | Others(提取码:DLVC) | |
Uber-Text | 92K. Collecetd from Bing Maps Streetside. Many are house number, and some are text on signboards | Others(提取码:DLVC) | |
Art | 29K. Art is created to recognize Arbitrary-shaped Text. Many are perspective or curved texts. It also includes Totaltext and CTW1500, which contain many rotated or curved texts | Others(提取码:DLVC) | |
LSVT | 34K in English. LSVT is a Large-scale Streeet View Text dataset, collected from streets in China. We select those in english | Others(提取码:DLVC) | |
MLT19 | 46K in English. MLT19 is created to recognize Multi-Lingual Text. It consists of seven languages:Arabic, Latin, Chinese, Japanese, Korean, Bangla, and Hindi. We select those in english | Others(提取码:DLVC) | |
ReCTS | 23K in English. ReCTS is created for the Reading Chinese Text on Signboard competition. It contains many irregular texts arranged in various layouts or written with unique fonts. We select those in english | Others(提取码:DLVC) |
Regular Dataset | Irregular dataset | ||||||||
Model | Year | IIIT | SVT | IC13(857) | IC13(1015) | IC15(1811) | IC15(2077) | SVTP | CUTE |
CRNN | 2015 | 78.2 | 80.8 | - | 86.7 | - | - | - | - |
ASTER(L2R) | 2015 | 92.67 | 91.16 | - | 90.74 | 76.1 | - | 78.76 | 76.39 |
CombBest | 2019 | 87.9 | 87.5 | 93.6 | 92.3 | 77.6 | 71.8 | 79.2 | 74 |
ESIR | 2019 | 93.3 | 90.2 | - | 91.3 | - | 76.9 | 79.6 | 83.3 |
SE-ASTER | 2020 | 93.8 | 89.6 | - | 92.8 | 80 | 81.4 | 83.6 | |
DAN | 2020 | 94.3 | 89.2 | - | 93.9 | - | 74.5 | 80 | 84.4 |
RobustScanner | 2020 | 95.3 | 88.1 | - | 94.8 | - | 77.1 | 79.5 | 90.3 |
AutoSTR | 2020 | 94.7 | 90.9 | - | 94.2 | 81.8 | - | 81.7 | - |
Yang et al. | 2020 | 94.7 | 88.9 | - | 93.2 | 79.5 | 77.1 | 80.9 | 85.4 |
SATRN | 2020 | 92.8 | 91.3 | - | 94.1 | - | 79 | 86.5 | 87.8 |
SRN | 2020 | 94.8 | 91.5 | 95.5 | - | 82.7 | - | 85.1 | 87.8 |
GA-SPIN | 2021 | 95.2 | 90.9 | - | 94.8 | 82.8 | 79.5 | 83.2 | 87.5 |
PREN2D | 2021 | 95.6 | 94 | 96.4 | - | 83 | - | 87.6 | 91.7 |
Bhunia et al. | 2021 | 95.2 | 92.2 | - | 95.5 | - | 84 | 85.7 | 89.7 |
Luo et al. | 2021 | 95.6 | 90.6 | - | 96.0 | 83.9 | 81.4 | 85.1 | 91.3 |
VisionLAN | 2021 | 95.8 | 91.7 | 95.7 | - | 83.7 | - | 86 | 88.5 |
ABINet | 2021 | 96.2 | 93.5 | 97.4 | - | 86.0 | - | 89.3 | 89.2 |
MATRN | 2021 | 96.7 | 94.9 | 97.9 | 95.8 | 86.6 | 82.9 | 90.5 | 94.1 |