py-inkwell 0.0.7

Python library for document processing

Inkwell

Quickstart on Colab

Overview

Inkwell is a modular Python library for extracting information from PDF documents documents with state of the art Vision Language Models. We make use of layout understanding models to improve accuracy of Vision Language models. You can swap out the layout models or the vision language models pretty easily to create pipelines which work well for specific document layouts.

Inkwell uses the following models, with more integrations in the work

• Layout Detection: Faster RCNN, LayoutLMv3, Paddle
• Table Detection: Table Transformer
• Table Data Extraction: Phi3.5-Vision, Qwen2 VL 2B, Table Transformer, OpenAI 4o Mini
• OCR: Tesseract, PaddleOCR, Phi3.5-Vision, Qwen2 VL 2B

Installation

pip install py-inkwell

In addition, install detectron2

pip install git+https://github.com/facebookresearch/detectron2.git

Install Tesseract

For Ubuntu -

sudo apt install tesseract-ocr
sudo apt install libtesseract-dev

and, Mac OS

brew install tesseract

For GPUs, install flash attention for faster inference.

pip install flash-attn --no-build-isolation

Basic Usage

from inkwell.pipeline import Pipeline

pipeline = Pipeline()
document = pipeline.process("/path/to/file.pdf")

for page in document.pages:

    figures = page.image_fragments()
    tables = page.table_fragments()
    text_blocks = page.text_fragments()

    # Check the content of the image fragments
    for figure in figures:
        figure_image = figure.content.image
        print(f"Text in figure:\n{figure.content.text}")
    
    # Check the content of the table fragments
    for table in tables:
        table_image = table.content.image
        print(f"Table detected: {table.content.data}")

    # Check the content of the text blocks
    for text_block in text_blocks:
        text_block_image = text_block.content.image
        print(f"Text block detected: {text_block.content.text}")

Models/Frameworks currently available

Default models: We have defined a config class here, and we use the default CPU Config in the pipeline for best results. If you want to use the default GPU pipeline, you can instantiate it with the GPU config class.

from inkwell.pipeline import DefaultGPUPipelineConfig, Pipeline
config = DefaultGPUPipelineConfig()
pipeline = Pipeline(config=config)

Changing the configuration

If you want to change the default models, you can replace them with models listed below by passing them in the config during pipeline initialization:

from inkwell.pipeline import PipelineConfig, Pipeline
from inkwell.layout_detector import LayoutDetectorType
from inkwell.ocr import OCRType
from inkwell.table_detector import TableDetectorType, TableExtractorType

config = PipelineConfig(
    layout_detector=LayoutDetectorType.FASTER_RCNN,
    table_extractor=TableExtractorType.PHI3_VISION,
)

pipeline = Pipeline(config=config)

Advanced Customizations

You can add custom detectors and other components to the pipeline yourself - follow the instructions in the Custom Components notebook

Acknowledgements

We derived inspiration from several open-source libraries in our implementation, like Layout Parser and Deepdoctection. We would like to thank the contributors to these libraries for their work.