rm <a> in doc
-
+- [Key Information Extraction(KIE)](#key-information-extractionkie)
+ - [1. Quick Use](#1-quick-use)
+ - [2. Model Training](#2-model-training)
+ - [3. Model Evaluation](#3-model-evaluation)
+ - [4. Reference](#4-reference)
# Key Information Extraction(KIE)
@@ -6,13 +10,6 @@ This section provides a tutorial example on how to quickly use, train, and evalu
[SDMGR(Spatial Dual-Modality Graph Reasoning)](https://arxiv.org/abs/2103.14470) is a KIE algorithm that classifies each detected text region into predefined categories, such as order ID, invoice number, amount, and etc.
-
-* [1. Quick Use](#1-----)
-* [2. Model Training](#2-----)
-* [3. Model Evaluation](#3-----)
-
-<a name="1-----"></a>
-
## 1. Quick Use
[Wildreceipt dataset](https://paperswithcode.com/dataset/wildreceipt) is used for this tutorial. It contains 1765 photos, with 25 classes, and 50000 text boxes, which can be downloaded by wget:
@@ -37,7 +34,6 @@ The visualization results are shown in the figure below:
<img src="./imgs/0.png" width="800">
</div>
-<a name="2-----"></a>
## 2. Model Training
Create a softlink to the folder, `PaddleOCR/train_data`:
@@ -51,7 +47,6 @@ The configuration file used for training is `configs/kie/kie_unet_sdmgr.yml`. Th
```shell
python3.7 tools/train.py -c configs/kie/kie_unet_sdmgr.yml -o Global.save_model_dir=./output/kie/
```
-<a name="3-----"></a>
## 3. Model Evaluation
@@ -61,7 +56,7 @@ After training, you can execute the model evaluation with the following command:
python3.7 tools/eval.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=./output/kie/best_accuracy
```
-**Reference:**
+## 4. Reference
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