代码拉取完成，页面将自动刷新
Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. While there have been great advances in STR methods, current methods still fail to recognize texts in arbitrary shapes, such as heavily curved or rotated texts, which are abundant in daily life (e.g. restaurant signs, product labels, company logos, etc). This paper introduces a novel architecture to recognizing texts of arbitrary shapes, named Self-Attention Text Recognition Network (SATRN), which is inspired by the Transformer. SATRN utilizes the self-attention mechanism to describe two-dimensional (2D) spatial dependencies of characters in a scene text image. Exploiting the full-graph propagation of self-attention, SATRN can recognize texts with arbitrary arrangements and large inter-character spacing. As a result, SATRN outperforms existing STR models by a large margin of 5.7 pp on average in "irregular text" benchmarks. We provide empirical analyses that illustrate the inner mechanisms and the extent to which the model is applicable (e.g. rotated and multi-line text). We will open-source the code.
cd /satrn/pytorch/base/csrc
bash clean.sh
bash build.sh
bash install.sh
cd ..
pip3 install -r requirements.txt
mkdir data
cd data
Reffering to MMOCR Docs to prepare datasets. Datasets path would look like below:
├── mixture
│ ├── coco_text
│ │ ├── train_label.txt
│ │ ├── train_words
│ ├── icdar_2011
│ │ ├── training_label.txt
│ │ ├── Challenge1_Training_Task3_Images_GT
│ ├── icdar_2013
│ │ ├── train_label.txt
│ │ ├── test_label_1015.txt
│ │ ├── test_label_1095.txt
│ │ ├── Challenge2_Training_Task3_Images_GT
│ │ ├── Challenge2_Test_Task3_Images
│ ├── icdar_2015
│ │ ├── train_label.txt
│ │ ├── test_label.txt
│ │ ├── ch4_training_word_images_gt
│ │ ├── ch4_test_word_images_gt
│ ├── III5K
│ │ ├── train_label.txt
│ │ ├── test_label.txt
│ │ ├── train
│ │ ├── test
│ ├── ct80
│ │ ├── test_label.txt
│ │ ├── image
│ ├── svt
│ │ ├── test_label.txt
│ │ ├── image
│ ├── svtp
│ │ ├── test_label.txt
│ │ ├── image
│ ├── Syn90k
│ │ ├── shuffle_labels.txt
│ │ ├── label.txt
│ │ ├── label.lmdb
│ │ ├── mnt
│ ├── SynthText
│ │ ├── alphanumeric_labels.txt
│ │ ├── shuffle_labels.txt
│ │ ├── instances_train.txt
│ │ ├── label.txt
│ │ ├── label.lmdb
│ │ ├── synthtext
│ ├── SynthAdd
│ │ ├── label.txt
│ │ ├── label.lmdb
│ │ ├── SynthText_Add
│ ├── TextOCR
│ │ ├── image
│ │ ├── train_label.txt
│ │ ├── val_label.txt
│ ├── Totaltext
│ │ ├── imgs
│ │ ├── annotations
│ │ ├── train_label.txt
│ │ ├── test_label.txt
│ ├── OpenVINO
│ │ ├── image_1
│ │ ├── image_2
│ │ ├── image_5
│ │ ├── image_f
│ │ ├── image_val
│ │ ├── train_1_label.txt
│ │ ├── train_2_label.txt
│ │ ├── train_5_label.txt
│ │ ├── train_f_label.txt
│ │ ├── val_label.txt
python3 train.py configs/models/satrn_academic.py
bash dist_train.sh configs/models/satrn_academic.py 8
| approach | GPUs | train mem | train FPS |
|---|---|---|---|
| satrn | BI100x8 | 14.159G | 549.94 |
| dataset | acc |
|---|---|
| IIIT5K | 94.5 |
| IC15 | 83.3 |
| SVTP | 88.4 |
| Convergence criteria | Configuration (x denotes number of GPUs) | Performance | Accuracy | Power（W） | Scalability | Memory utilization（G） | Stability |
|---|---|---|---|---|---|---|---|
| 0.841 | SDK V2.2,bs:128,8x,fp32 | 630 | 88.4 | 166*8 | 0.98 | 28.5*8 | 1 |
此处可能存在不合适展示的内容，页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容，可点击提交进行申诉，我们将尽快为您处理。