Gap : NLP/CV Data Engineering Framework

Natural Language Processing for PDF, TIFF, and Camera Captured Documents, and

Computer Vision Processing for Images

Framework

The Gap NLP/CV data engineering framework provides an easy to get started into the world of machine learning for your unstructured data in PDF documents, scanned documents, TIFF facsimiles and camera captured documents, and your image data in image files and image repositories.

NLP , v0.9.3 (Pre-launch: alpha)

Automatic OCR of scanned PDF and camera captured images.
Automatic Text Extraction from documents.
Automatic Syntax Analysis.
Optional Romanization of Latin-1 diacritic characters.
Optional Spell Correction.
Programmatic control for data extraction or redaction (de-identification).
- Names, Addresses, Proper Places
- Social Security Numbers, Data of Birth, Gender, Age
- Telephone Numbers
- Numerical Information (e.g., medical, financial, …) and units of measurement.
- Unit conversion from US Standard to Metric, and vice-versa
- Unicode character recognition
Machine Training of Document and Page Classification.
Asynchronous processing of documents.
Automatic generation of NLP machine learning ready data.

CV , v0.9.4 (Pre-launch: beta)

Automatic storage and retrieval with high performance HDF5 files.
Automatic handling of mixed channels (grayscale, RGB and RGBA) and pixel size.
Programmatic control of resizing.
Programmatic control of conversion into machine learning ready data format: decompression, normalize, flatten.
Programmatic control of minibatch generation.
Programmatic control of image augmentation.
Asynchronous processing of images.
Automatic generation of CV machine learning ready data.

The framework consists of a sequence of Python modules which can be retrofitted into a variety of configurations. The framework is designed to fit seamlessly and scale with an accompanying infrastructure. To achieve this, the design incorporates:

Problem and Modular Decomposition utilizing Object Oriented Programming Principles.
Isolation of Operations and Parallel Execution utilizing Functional Programming Principles.
High Performance utilizing Performance Optimized Python Structures and Libraries.
High Reliability and Accuracy using Test Driven Development Methodology.

Audience

This framework is ideal for any organization planning to do:

Data extraction from their repository of documents into an RDBMS system for CART analysis, linear/logistic regressions,
or generating word vectors for natural language deep learning (DeepNLP).
Generating machine learning ready datan from their repository of images for computer vision.

License

The source code is made available under the Apache 2.0 license: Apache 2.0

Prerequites

The Gap framework extensively uses a number of open source applications/modules. The following applications and modules will be downloaded onto your computer/laptop when the package OR setup file is installed.

Artifex's Ghostscript - extracting text from text PDF
ImageMagic's Magick - extracting image from scanned PDF
Google's Tesseract - OCR of scanned/image captured text
NLTK (Natural Language Toolkit) - stemming/lemmatizer/parts of speech annotation
unidecode - romanization of latin character codes
numpy - high performance in-memory arrays (tensors)
HDF5 - high performance of on-disk data (tensors) access
openCV - image manipulation and processing for computer vision
imutils - image manipulation for computer vision

Pip Installation:

The Gap framework is supported on Windows, MacOS, and Linux. It has been packaged for distribution via PyPi on launch.

install miniconda
(optional)
- Create an environment with: conda create -n gap python==3.7 jupyter
- Activate: source activate gap
- Deactivate: source deactivate
install GapML:
- pip install gapml

Setup.py Installation:

To install GapML via setup.py:

clone from the Github repo.
- git clone https://github.com/gapml/CV.git
install the GapML setup file.
- python setup.py install

Modules

The framework provides the following pipeline of modules to support your data and knowledge extraction from both digital and scanned PDF documents, TIFF facsimiles and image captured documents.

VISION

The splitter module is the CV entry point into the pipeline. It consists of a Images and Image class. The Images class handles the storage and (random access) batch retrieval of CV machine learning ready data, using open source openCV image processing, numpy high performance arrays (tensors) and HDF5 high performance disk (tensor) access. The Image class handles preprocessing of individual images into CV machine learning ready data. The batch and image preprocessing can be done synchronously or asynchronously, where in the latter case an event handler signals when the preprocessing of an image or batch has been completed and the machine learning ready data is accessible.

The vision module handles:

Mixed image size, format, resolution, number of channels
Decompression, Resizing, Normalizing, Flattening

Specification

User's Guide

The User's (Programming) Quick Start Guide can be found here

Releases

Testing

The GAP framework is developed using Test Driven Development methodology. The automated unit tests for the framework use pytest, which is a xUnit style form of testing (e.g., jUnit, nUnit, jsUnit, etc).

Installation and Documentation

The pytest application can be installed using pip:

pip install pytest

Online documentation for pytest

Execution

The following are the pre-built automated unit tests, which are located under the subdirectory tests:

document_test.py    # Tests the Document Class in the Splitter Module
page_test.py        # Tests the Page Class in the Splitter Module
words_test.py       # Tests the Words and Addresses Class in the Syntax Module
segment_test.py     # Tests the Segment Class in the Segment Module
image_test.py       # Tests the Image and Images Class in the Vision Module

The automated tests are executed as follows:

pytest -v image_test.py

Code Coverage

Information on the percent of code that is covered (and what source lines not covered) by the automated tests is obtained using pytest-cov. This version of pytest is installed using pip:

pip install pytest-cov

Testing with code coverage is executed as follows:

pytest --cov=gampml.vision image_test.py

    Statements=652, Missed=56, Percent Covered: 91%

virtualdvid / CV