# ChartOCR

**Repository Path**: esheeper/ChartOCR

## Basic Information

- **Project Name**: ChartOCR
- **Description**: ................
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-03-26
- **Last Updated**: 2024-03-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DeepRule
Compete code of DeepRule
## Getting Started
Please first install [Anaconda](https://anaconda.org) and create an Anaconda environment using the provided package list.
```
 conda create  --name DeepRule --file DeepRule.txt
```

After you create the environment, activate it.
```
source activate DeepRule
```

Our current implementation only supports GPU so you need a GPU and need to have CUDA installed on your machine.

### Compiling Corner Pooling Layers
You need to compile the C++ implementation of corner pooling layers. 
Please check the latest CornerNet on github if you find problems.

Just fix some warning bugs. (Updated on 06/30/2023).

https://github.com/princeton-vl/CornerNet
```
cd <CornerNet dir>/models/py_utils/_cpools/
python setup.py build_ext --inplace
```

### Compiling NMS
You also need to compile the NMS code (originally from [Faster R-CNN](https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/nms/cpu_nms.pyx) and [Soft-NMS](https://github.com/bharatsingh430/soft-nms/blob/master/lib/nms/cpu_nms.pyx)).
```
cd <CornerNet dir>/external
make
```

### Installing MS COCO APIs
You also need to install the MS COCO APIs.
```
pip install pycocotools
```

### Downloading CHARTEX Data
- https://huggingface.co/datasets/asbljy/DeepRuleDataset/tree/main
- Unzip the file to the data path
### Data Description (Updated on 11/21/2021)
- For Pie data<br/>
{"image_id": 74999, "category_id": 0, "bbox": [135.0, 60.0, 132.0, 60.0, 134.0, 130.0], "area": 105.02630551355209, "id": 433872}<br/>
The meaning of the bbox is [center_x, center_y, edge_1_x, edge_1_y, edge_2_x, edge_2_y]<br/>
It’s the three critical points for a sector of the pie graph.

- For the line data<br/>
{"image_id": 120596, "category_id": 0, "bbox": [137.0, 131.0, 174.0, 113.0, 210.0, 80.0, 247.0, 85.0], "area": 0, "id": 288282}<br/>
The meaning of the bbox is [d_1_x, d_1_y, …., d_n_x,d_n_y]<br/>
It’s the data points for a line in the image with image_id.<br/>
instancesLineClsEx is used for training the LineCls.

- For the Bar data<br/>
Just the bounding box of the bars.

- For the cls data<br/>
Just the bounding box.<br/>
But different category_id refers to different components like the draw area, title and legends.
### OCR API (Updated on 08/17/2022)
I am longger working at the microsoft, many features rely on the webservice may be out of date.
The origninal OCR API requests the AZURE service. For people who do not have the AZURE service, pytesseract python pacakge may be a good replacment.
However, you need to rewrite ocr_result(image_path) funtion. The key output of this function is the bounding box of the words and the str version of the words.
E.g., word_info["text"]='Hello', word_info["boundingBox"] = [1, 2, 67, 78]
The boudningBox is the topleft_x, topleft_y, bottomleft_x, bottomlef_y.
### environment (Updated on 05/18/2023)
If you are not able to install the old environment, try the new env file “requirements-2023.txt”
### Downloading Trained File
- [data link](https://huggingface.co/datasets/asbljy/DeepRuleDataset/tree/main)
- Unzip the file to current root path 
## Training and Evaluation
To train and evaluate a network, you will need to create a configuration file, which defines the hyperparameters, and a model file, which defines the network architecture. The configuration file should be in JSON format and placed in `config/`. Each configuration file should have a corresponding model file in `models/`. i.e. If there is a `<model>.json` in `config/`, there should be a `<model>.py` in `models/`. There is only one exception which we will mention later.
The cfg file names of our proposed modules are as follows:

Bar: CornerNetPureBar

Pie: CornerNetPurePie

Line: CornerNetLine

Query: CornerNetLineClsReal

Cls: CornerNetCls

To train a model:
```
python train.py --cfg_file <model> --data_dir <data path> 
e.g. 
python train_chart.py --cfg_file CornerNetBar --data_dir /home/data/bardata(1031)
```

To use the trained model as a web server pipeline:
```
python manage.py runserver 8800
```
Access localhost:8800 to interact.

If you want to test batch of data directly, here you have to pre-assign the type of charts.
```
python test_pipe_type_cloud.py --image_path <image_path> --save_path <save_path> --type <type>
e.g.
python test_pipe_type_cloud.py --image_path /data/bar_test --save_path save --type Bar
```