Overview¶
Weights¶
Here are the list of weights available for Inference.
For the ease of reference, some weights may have shorter aliases, which will be
separated by /
in the table.
For example, “DB_r18 / dbnet_resnet18_fpnc_1200e_icdar2015
” means that you can
use either DB_r18
or dbnet_resnet18_fpnc_1200e_icdar2015
to initialize the Inferencer:
>>> from mmocr.apis import TextDetInferencer
>>> inferencer = TextDetInferencer(model='DB_r18')
>>> # equivalent to
>>> inferencer = TextDetInferencer(model='dbnet_resnet18_fpnc_1200e_icdar2015')
Text Detection¶
Model |
README |
ICDAR2015 (hmean-iou) |
CTW1500 (hmean-iou) |
Totaltext (hmean-iou) |
---|---|---|---|---|
|
0.8169 |
- |
- |
|
|
0.8504 |
- |
- |
|
|
0.8543 |
- |
- |
|
|
0.8644 |
- |
- |
|
|
- |
- |
0.8182 |
|
|
0.8622 |
- |
- |
|
|
0.8684 |
- |
- |
|
|
0.8882 |
- |
- |
|
|
- |
0.7458 |
- |
|
|
- |
0.7562 |
- |
|
|
0.8182 |
- |
- |
|
|
0.8513 |
- |
- |
|
|
- |
0.8467 |
- |
|
|
- |
0.8488 |
- |
|
|
- |
0.8192 |
- |
|
|
0.8528 |
- |
- |
|
|
0.8604 |
- |
- |
|
|
- |
- |
0.8134 |
|
|
- |
0.777 |
- |
|
|
0.7848 |
- |
- |
|
|
- |
0.7793 |
- |
|
|
- |
0.8037 |
- |
|
|
0.7998 |
- |
- |
|
|
0.8478 |
- |
- |
|
|
- |
0.8286 |
- |
|
|
- |
0.8529 |
- |
Text Recognition¶
Note
Avg is the average on IIIT5K, SVT, ICDAR2013, ICDAR2015, SVTP, CT80.
Model |
README |
Avg (word_acc) |
IIIT5K (word_acc) |
SVT (word_acc) |
ICDAR2013 (word_acc) |
ICDAR2015 (word_acc) |
SVTP (word_acc) |
CT80 (word_acc) |
---|---|---|---|---|---|---|---|---|
|
0.88 |
0.95 |
0.91 |
0.94 |
0.79 |
0.84 |
0.84 |
|
|
0.91 |
0.96 |
0.94 |
0.95 |
0.81 |
0.89 |
0.88 |
|
|
0.86 |
0.94 |
0.89 |
0.93 |
0.77 |
0.81 |
0.85 |
|
|
0.70 |
0.81 |
0.81 |
0.87 |
0.56 |
0.61 |
0.57 |
|
|
0.88 |
0.95 |
0.90 |
0.95 |
0.76 |
0.85 |
0.89 |
|
|
0.83 |
0.92 |
0.88 |
0.94 |
0.72 |
0.78 |
0.75 |
|
|
0.87 |
0.95 |
0.88 |
0.95 |
0.76 |
0.80 |
0.89 |
|
|
0.87 |
0.95 |
0.90 |
0.94 |
0.74 |
0.80 |
0.89 |
|
|
0.86 |
0.86 |
0.90 |
0.94 |
0.75 |
0.85 |
0.89 |
|
|
0.87 |
0.86 |
0.92 |
0.94 |
0.74 |
0.84 |
0.90 |
|
|
0.87 |
0.95 |
0.89 |
0.93 |
0.76 |
0.81 |
0.87 |
|
|
0.88 |
0.95 |
0.88 |
0.94 |
0.76 |
0.83 |
0.90 |
|
|
0.87 |
0.96 |
0.87 |
0.94 |
0.77 |
0.81 |
0.89 |
|
|
0.90 |
0.96 |
0.92 |
0.96 |
0.80 |
0.88 |
0.90 |
|
|
0.88 |
0.94 |
0.90 |
0.96 |
0.79 |
0.86 |
0.85 |
Statistics¶
Number of checkpoints: 48
Number of configs: 49
Number of papers: 19
ALGORITHM: 19
Text Detection Models¶
Number of checkpoints: 29
Number of configs: 29
Number of papers: 8
[ALGORITHM] Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection
[ALGORITHM] Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network
[ALGORITHM] Fourier Contour Embedding for Arbitrary-Shaped Text Detection
[ALGORITHM] Mask R-CNN
[ALGORITHM] Real-Time Scene Text Detection With Differentiable Binarization and Adaptive Scale Fusion
[ALGORITHM] Real-Time Scene Text Detection With Differentiable Binarization
[ALGORITHM] Shape Robust Text Detection With Progressive Scale Expansion Network
[ALGORITHM] Textsnake: A Flexible Representation for Detecting Text of Arbitrary Shapes
Text Recognition Models¶
Number of checkpoints: 16
Number of configs: 17
Number of papers: 9
[ALGORITHM] Aster: An Attentional Scene Text Recognizer With Flexible Rectification
[ALGORITHM] Master: Multi-Aspect Non-Local Network for Scene Text Recognition
[ALGORITHM] Nrtr: A No-Recurrence Sequence-to-Sequence Model for Scene Text Recognition
[ALGORITHM] On Recognizing Texts of Arbitrary Shapes With 2d Self-Attention
[ALGORITHM] Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition
[ALGORITHM] Robustscanner: Dynamically Enhancing Positional Clues for Robust Text Recognition
[ALGORITHM] Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
[ALGORITHM] Svtr: Scene Text Recognition With a Single Visual Model
Key Information Extraction Models¶
Number of checkpoints: 3
Number of configs: 3
Number of papers: 1