Introduction

The University of Nebraska-Lincoln's (UNL) Aida digital libraries research team and the Library of Congress (LC) collaborated on a "summer of machine learning" in 2019 to explore machine learning techniques for extending the accessibility of digital collections. The UNL team developed a number of prototype explorations over multiple iterations to investigate a range of questions and issues related to the digital materials, the LC's collections, and to machine learning practices in cultural heritage organizations. The UNL team employed a variety of machine learning approaches such as back-propagation neural network-based classifiers and deep learning approaches, including convolutional neural networks. More specifically, these projects involve VGG16, ResNeXt, dhSegment, and a fusion network combining ResNeXt and U-Net.

This repository includes the code developed and used across the team's explorations.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

For Exploration - Document Segmentation, the required software systems and libraries are:

• Anaconda >= 4.3
• Python >= 3.6
• TensorFlow 1.13
• CUDA 10.0 [if training on GPU]
• imageio >= 2.5
• pandas >= 0.24.2
• shapely >= 1.6.4
• scikit-learn >= 0.20.3
• scikit-image >= 0.15.0
• opencv-python >= 4.0.1
• tqdm >= 4.31.1
• sacred 0.7.4
• requests >= 2.21.0
• click >= 7.0

For Exploration - Graphic Element Classification and Text Extraction and Exploration - Digitization Type Differentiation, the required software systems and libraries are:

• Python 3.7
• MXNet 1.5
• CUDA 10.0 [if training on GPU]
• Matplotlib 3.1.1
• opencv-python 4.1
• numpy 1.17

For Exploration - Document Type Classification, the required software systems and libraries are:

• Anaconda >= 4.3
• Python >= 3.6
• TensorFlow 1.13
• CUDA 10.0 [if training on GPU]
• opencv-python >= 4.0.1
• numpy >= 1.16.2
• scikit-learn >= 0.20.3
• scikit-image >= 0.15.0
• matplotlib >= 1.4.3
• pandas >= 0.24.2
• seaborn 0.9.0

For Exploration - Document Image Quality Assessment, the required software systems and libraries are:

• Python 3.7
• scipy 1.3.1
• opencv-python 4.1
• skimage 0.15

Installing

Step-by-step instructions on how to install required software systems and libraries for each project

For Exploration - Document Segmentation

1. Download Python 3.6 from https://www.python.org/downloads/
2. Download CUDA 10.0 from https://developer.nvidia.com/cuda-toolkit-archive
3. Install Anaconda or Miniconda (installation procedure)
4. Open Terminal (for MacOS), Command-Line (for Windows)
5. Go to the codebase/Exploration - Document Segmentation folder
6. Create a virtual environment and activate it

conda create -n segmentation python=3.6
source activate segmentation

7. Install packages

python setup.py install

For Exploration - Graphic Element Classification and Text Extraction and Exploration - Digitization Type Differentiation

1. Download Python 3.7 from https://www.python.org/downloads/
2. Download CUDA 10.0 from https://developer.nvidia.com/cuda-toolkit-archive
3. Install downloaded installation file
4. Open Terminal (for macOS), Command-Line (for Windows)
5. Install MXNet

pip install 'mxnet-cu100==1.5.1'

6. Install Matplotlib

python -m pip install -U 'matplotlib==3.1.1'

7. Install opencv-python

pip install 'opencv-python==4.1'

8. Install numpy

pip install 'numpy==1.17'

For Exploration - Document Type Classification

1. Download Python 3.6 from https://www.python.org/downloads/
2. Download CUDA 10.0 from https://developer.nvidia.com/cuda-toolkit-archive
3. Install Anaconda or Miniconda (installation procedure)
4. Open Terminal (for MacOS), Command-Line (for Windows)
5. Go to the codebase/Exploration - Digitization Type Differentiation folder
6. Create a virtual environment and activate it

conda create -n classification python=3.6
source activate classification

7. Install packages

python setup.py install

For Exploration - Document Image Quality Assessment

1. Download Python 3.7 from https://www.python.org/downloads/
2. Install downloaded installation file
3. Open Terminal (for macOS), Command-Line (for Windows)
4. Install scipy

pip install 'scipy==1.3.1'

5. Install opencv-python

pip install 'opencv-python==4.1'

6. Install skimage

pip install 'scikit-image==0.15'

Running the demonstrations

Exploration - Document Segmentation:

1. Download all files in demo/Exploration - Document Segmentation https://git.unl.edu/unl_loc_summer_collab/codebase/tree/master/demo/Exploration%20-%20Document%20Segmentation
2. Install required softwares and libraries
3. Download one of the following dataset: (1) https://git.unl.edu/unl_loc_summer_collab/labeled_data/tree/master/ENP_500 or (2) https://git.unl.edu/unl_loc_summer_collab/labeled_data/tree/master/difficulty_collection, for segmentation or clustering task, respectively
4. Copy the downlaoded folder to the downloaded 'Exploration - Document Segmentation' folder
5. Run one of the following command, depending on the purpose

# Activate virtual environment
source activate segmentation
# For segmentation task
python demo_segmentation.py
# For clustering task
python demo_clustering.py

Exploration - Graphic Element Classification and Text Extraction:

1. Download all files in demo/Exploration - Graphic Element Classification and Text Extraction https://git.unl.edu/unl_loc_summer_collab/codebase/tree/master/demo/Exploration%20-%20Graphic%20Element%20Classification%20and%20Text%20Extraction
2. Install the required software and libraries
3. Download dataset from https://git.unl.edu/unl_loc_summer_collab/labeled_data/tree/master/BeyondWord_orginal_resolution
4. Copy the downloaded folder to the downloaded 'Exploration - Graphic Element Classification and Text Extraction' folder
5. Run the evaluation script

python eval.py

Exploration - Document Type Classification:

1. Download all files in demo/Exploration - Document Type Classification https://git.unl.edu/unl_loc_summer_collab/codebase/tree/master/demo/Exploration%20-%20Document%20Type%20Classification
2. Install required softwares and libraries
3. Download dataset from https://git.unl.edu/unl_loc_summer_collab/labeled_data/tree/master/suffrage_1002
4. Copy the downlaoded folder to the downloaded 'Exploration - Document Type Classification' folder
5. Run the demonstration script

# Activate virtual environment
source activate Exploration - Document Type Classification
# Run demonstration
python demo_classification.py

Exploration - Digitization Type Differentiation:

1. Download all files in [demo/Exploration - Digitization Type Differentiation] https://git.unl.edu/unl_loc_summer_collab/codebase/tree/master/demo/Exploration%20-%20Digitization%20Type%20Differentiation
2. Install the required software and libraries
3. Download dataset from https://git.unl.edu/unl_loc_summer_collab/labeled_data/tree/master/micrpfilm_scanning
4. Copy the downloaded folder to the downloaded 'Exploration - Digitization Type Differentiation' folder
5. Run the evaluation script

python eval.py

Breaking down into end-to-end tests

Please read the README file inside each project folder for a description of each end-to-end test.

• Exploration - Document Segmentation: https://git.unl.edu/unl_loc_summer_collab/codebase/tree/master/demo/Exploration%20-%20Document%20Segmentation
• Exploration - Digitization Type Differentiation: <https://git.unl.edu/unl_loc_summer_collab/codebase/tree/master/Exploration%20-%20Graphic Element%20Classification%20and%20Text%20Extraction>
• Exploration - Document Type Classification: https://git.unl.edu/unl_loc_summer_collab/codebase/tree/master/Exploration%20-%20Document%20Type%20Classification
• Exploration - Document Image Quality Assessment: https://git.unl.edu/unl_loc_summer_collab/codebase/tree/master/Exploration%20-%20Document%20Image%20Quality%20Assessment
• Exploration - Digitization Type Differentiation: https://git.unl.edu/unl_loc_summer_collab/codebase/tree/master/Exploration%20-%20Digitization%20Type%20Differentiation

Built With

• Python - The programming language
• CUDA Toolkit - Enable GPU for model training
• MXNet - Deep learning framework
• TensorFLow - Deep learning framework
• Matlab - Math, graphics, programming platform

Contributing

Authors

• Yi Liu - research associate and developer
• Chulwoo (Mike) Pack - research associate and developer
• Elizabeth Lorang - senior adviser
• Leen-Kiat Soh - senior adviser
• Ashlyn Stewart - research assistant

License

This project is licensed under the GPL License - see the LICENSE file for details