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---
title: alps
app_file: app.py
sdk: gradio
sdk_version: 4.44.0
---
# Alps
Pipeline for OCRing PDFs and tables
This repository contains different OCR methods using various libraries/models.
## Running gradio:
`python app.py` in terminal
## Installation :
Build the docker image and run the contianer
Clone this repository and Install the required dependencies:
```
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu117
apt install weasyprint
```
Note: You need a GPU to run this code.
## Example Usage
Run python main.py inside the directory. Provide the path to the test file (the file must be placed inside the repository,and the file path should be relative to the repository (alps)). Next, provide the path to save intermediate outputs from the run (draw cell bounding boxes on the table, show table detection results in pdf), and specify which component to run.
outputs are printed in terminal
```
usage: main.py [-h] [--test_file TEST_FILE] [--debug_folder DEBUG_FOLDER] [--englishFlag ENGLISHFLAG] [--denoise DENOISE] ocr
```
Description of the component:
### ocr1
ocr1
Input: Path to a PDF file
Output: Dictionary of each page and list of line_annotations. List of LineAnnotations contains bboxes for each line and List of its children wordAnnotation. Each wordAnnotation contains bboxes and text inside.
What it does: Runs Ragflow textline detector + OCR with DocTR
Example:
```
python main.py ocr1 --test_file TestingFiles/OCRTest1German.pdf --debug_folder ./res/ocrdebug1/
python main.py ocr1 --test_file TestingFiles/OCRTest3English.pdf --debug_folder ./res/ocrdebug1/ --englishFlag True
```
### table1
Input : file path to an image of a cropped table
Output: Parsed table in HTML form
What it does: Uses Unitable + DocTR
```
python main.py table1 --test_file cropped_table.png --debug_folder ./res/table1/
```
### table2
Input: File path to an image of a cropped table
Output: Parsed table in HTML form
What it does: Uses Unitable
```
python main.py table2 --test_file cropped_table.png --debug_folder ./res/table2/
```
### pdftable1
Input: PDF file path
Output: Parsed table in HTML form
What it does: Uses Unitable + DocTR
```
python main.py pdftable1 --test_file TestingFiles/OCRTest5English.pdf --debug_folder ./res/table_debug1/
python main.py pdftable3 --test_file TestingFiles/TableOCRTestEnglish.pdf --debug_folder ./res/poor_relief2
```
### pdftable2 :
Input: PDF file path
Output: Parsed table in HTML form
What it does: Detects table and parses them, Runs Full Unitable Table detection
```
python main.py pdftable2 --test_file TestingFiles/OCRTest5English.pdf --debug_folder ./res/table_debug2/
```
### pdftable3
Input: PDF file path
Output: Parsed table in HTML form
What it does: Detects table with YOLO, Unitable + DocTR
### pdftable4
Input: PDF file path
Output: Parsed table in HTML form
What it does: Detects table with YOLO, Runs Full doctr Table detection
python main.py pdftable4 --test_file TestingFiles/TableOCRTestEasier.pdf --debug_folder ./res/table_debug3/
## bbox
They are ordered as ordered as [xmin,ymin,xmax,ymax] . Cause the coordinates starts from (0,0) of the image which is upper left corner
xmin ymim - upper left corner
xmax ymax - bottom lower corner
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