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OCR research
ocr-sota's Introduction
Recognition - SOTA Performance (only unconstrained lexicon-free)
- TTM : Total-Text (multi-oriented)
- TTC : Total-Text (curved)
- 학습셋
- SK : Synth90K
- ST : SynthText
Paper |
year |
SVT |
IIIT5k |
IC03 |
IC13 |
SVTP |
CUTE80 |
IC15 |
TTM |
TTC |
비고 |
학습셋 |
SOTA |
|
91.5 |
94.0 |
96.7 |
95.8 |
86.6 |
88.5 |
79.4 |
76.3 |
66.7 |
|
|
[1] CRNN |
15.07 |
80.8 |
78.2 |
89.4 |
86.7 |
66.8 |
54.9 |
|
|
|
base:CRNN |
SK |
[10] RARE |
16.x |
81.9 |
81.9 |
90.1 |
88.6 |
71.8 |
|
|
|
|
Rectification |
SK |
[9] STAR-Net |
16.x |
83.6 |
83.3 |
89.9 |
89.1 |
73.5 |
|
|
|
|
Rectification |
SK |
[3] |
18.x |
87.1 |
89.4 |
94.7 |
94.0 |
73.9 |
62.5 |
|
|
|
GAN |
|
[4] ASTER |
18.x |
89.5 |
93.4 |
94.5 |
91.8 |
78.5 |
79.5 |
76.1 |
|
|
Rectification |
|
[5] AON |
18.03 |
82.8 |
87.0 |
91.5 |
|
73.0 |
76.8 |
68.2 |
|
|
|
|
[6] |
18.05 |
87.5 |
88.3 |
94.6 |
94.4 |
|
|
73.9 |
|
|
|
|
[2] |
18.12 |
88.6 |
94.0 |
93.6 |
93.2 |
80.6 |
88.5 |
77.1 |
76.3 |
66.7 |
|
SK+ST |
[7] ESIR |
18.12 |
90.2 |
93.3 |
|
91.3 |
79.6 |
83.3 |
76.9 |
|
|
Rectification |
SK+ST |
[8] MORAN V1 |
19.01 |
88.3 |
91.2 |
95.0 |
92.4 |
76.1 |
77.4 |
|
|
|
Rectification |
SK+ST |
[8] MORAN V2 |
19.01 |
88.3 |
93.4 |
94.2 |
93.2 |
79.7 |
81.9 |
|
|
|
Rectification |
SK+ST |
[11] NRTR |
19.10 |
91.5 |
90.1 |
95.4 |
95.8 |
86.6 |
80.9 |
79.4 |
|
|
|
|
[12] SATRN |
19.10 |
91.3 |
92.8 |
96.7 |
94.1 |
86.5 |
87.8 |
79.0 |
|
|
|
|
- [1] An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
- [2] Recurrent Calibration Network for Irregular Text Recognition
- [3] Synthetically Supervised Feature Learning for Scene Text Recognition : [paper][review]
- [4] ASTER: An Attentional Scene Text Recognizer with Flexible Rectification # Rectification
- [5] Arbitrarily-oriented text recognition
- [6] Edit Probability for Scene Text Recognition
- [7] ESIR: End-to-end Scene Text Recognition via Iterative Rectification # Rectification
- [8] MORAN: A Multi-Object Rectified Attention Network for Scene Text Recognition # Rectification
- [9] STAR-Net: A SpaTial Attention Residue Network for Scene Text Recognition
- [10] RARE: Robust Scene Text Recognition with Automatic Rectification
- [11] NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition # self attention
- [12] SATRN: On Recognizing Texts of Arbitrary Shapes with 2D Self-attention # self attention
Papers's training Information : 링크
- R : Recall
- P : Precision
- F : F-measure
- MS : MSRA-TD50
- TT : Total-Text
- CT : CTW1500
Paper |
year |
IC13-R |
IC13-P |
IC13-F |
IC15-R |
IC15-P |
IC15-F |
MS-R |
MS-P |
MS-F |
TT-R |
TT-P |
TT-F |
CT-R |
CT-P |
CT-F |
SOTA |
|
95.0 |
97.4 |
0.925 |
91.6 |
0.9185 |
0.8984 |
83.0 |
88.2 |
81.7 |
82.8 |
87.6 |
82.9 |
81.1 |
86.0 |
80.7 |
[5] |
16.04 |
0.78 |
0.88 |
0.83 |
0.43 |
0.71 |
0.54 |
0.67 |
0.83 |
0.74 |
|
|
|
|
|
|
[1] EAST |
17.07 |
87.53 |
93.34 |
90.34 |
78.33 |
83.27 |
80.78 |
67.43 |
87.28 |
76.08 |
36.2 |
50.0 |
42.0 |
49.1 |
78.7 |
60.4 |
[7] |
17.09 |
0.86 |
0.88 |
0.87 |
0.73 |
0.80 |
0.77 |
|
|
|
|
|
|
|
|
|
[3] PixelLink |
18.01 |
88.6 |
87.5 |
88.1 |
82.0 |
85.5 |
83.7 |
83.0 |
73.2 |
77.8 |
54.41 |
59.89 |
57.02 |
|
|
|
[8] FOTS |
18.01 |
|
|
0.925 |
0.8792 |
0.9185 |
0.8984 |
|
|
|
|
|
|
|
|
|
[9] |
18.07 |
87.1 |
91.5 |
89.2 |
|
|
|
77.4 |
83.0 |
80.1 |
|
|
|
|
|
|
[10] |
18.08 |
95.0 |
88.6 |
91.7 |
91.6 |
81.0 |
86.0 |
|
|
|
69.0 |
55.0 |
61.3 |
|
|
|
[6] |
18.11 |
90.5 |
93.8 |
92.1 |
85.8 |
88.7 |
87.2 |
|
|
|
82.8 |
83.0 |
82.9 |
|
|
|
[4] MSR |
19.01 |
88.5 |
91.8 |
90.1 |
|
|
|
76.7 |
87.4 |
81.7 |
73.0 |
85.2 |
78.6 |
77.8 |
83.8 |
80.7 |
[2] |
19.01 |
85.94 |
93.18 |
89.41 |
79.2 |
86.1 |
82.5 |
75.26 |
85.88 |
80.21 |
|
|
|
|
|
|
[11]CRAFT |
19.04 |
93.1 |
97.4 |
|
84.3 |
89.8 |
|
78.2 |
88.2 |
|
79.9 |
87.6 |
|
81.1 |
86.0 |
|
- [1] EAST: An Efficient and Accurate Scene Text Detector : [paper][review] : # U-Net
- [2] Detecting Text in the Wild with Deep Character Embedding Network
- [3] PixelLink: Detecting Scene Text via Instance Segmentation : [paper][review] : # U-Net
- [4] MSR: Multi-Scale Shape Regression for Scene Text Detection
- [5] Multi-Oriented Text Detection with Fully Convolutional Networks
- [6] Scene Text Detection with Supervised Pyramid Context Network : [paper][review]: #MASK R-CNN #Multi-Scale
- [7] Single Shot Text Detector with Regional Attention : #SSD
- [8] FOTS: Fast Oriented Text Spotting with a Unified Network : [paper][review]
- [9] Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping
- [10] Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
- [11] CRAFT: Character Region Awareness for Text Detection, # F-Measure대신에 H-Mean으로 표현했다.같은것인지
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