ocr-testbed

OCR Project

The purpose of this repo is to construct an ocr for detecting keywords from people's handwriting to make their notes searchable. Whole sentences do not need to be fully extracted, as long as a few unique keywords are extracted such that the extracted set of words are seo-able.

Environment

This package was developed in the following environment.

• Python 2.7.9
• Mac OSX 10.11.4, Ubuntu 14.04
• pip 8.1.1

Dependencies

This package is dependant on the packages provided in requirements.txt.

• numpy
• scipy
• matplotlib
• cv2

Optional development packages to install are provided in dev-requirements.txt. Precompiling the pure code with cython can allow for an extra boost in performance.

OpenCV

This package is heavily dependant on opencv, so be sure to install it first before installing the cv2 package in requirements.txt.

• Installing on Ubuntu/Debian
• Installing on Mac OS

Scikit Learn

If using the multilayer perceptron, the latest dev version of scikit learn (0.18.dev0) is required since it already contains an implementation of a multilayer perceptron that this package uses.

At the time of writing this, version 0.17.0 is provided on pypi, so this dev version cannot be installed via pip and will have to be installed by cloning the repo and just following the instructions in the repo for installing it.

Troubleshooting

If you have trouble installing numpy, scipy, or cv2, try installing them individually instead of together in the requirements.txt.

If you have trouble installing them in a virtualenv, try installing them on your global pip instead.

Installing

After cloning the repo, just run the following:

$ python setup.py develop  # Create executables

To make the ocr packageglobally availale on your computer via pip:

$ python setup.py bdist_wheel  # Create binary distribution
$ wheel install dist/ocr-{version}-py{2/3}-none-any.whl  # Install created wheel
$ wheel install-scripts ocr  # Enable package scripts

The first command with develop essentially does the same as the following three commands, but these commands allow for the package to be available globally through your computer in pip space like any other package installed via pip.

Usage

The package can be used to create classifiers and use these classifiers to classify characters extracted from an image.

Before extracting text from an image, you will need to create a classifier to classify each character extracted from the image.

K Nearest Neighbors

Create a knn classifier using handwritten training data in the given dir and save it in a file called classifier.p.

$ knn-create -t handwritten -d data/training/handwritten -s classifier.p
53.2258064516 %  # Success rate on testing data

Extract text from the given image using the classifier we just made. Resize the image to reduce computation time.

$ knn-extract data/test/sample3.jpg -p classifier.p --resize 0.25
6hhVL tS L mUYh kYttrYYr 6b h qYhcY  # Yeah, this isn't very good output

Multilayer Perceptron

Create an mlp classifier using shrinked, handwritten training data in the given dir and save it in a file called classifier.p.

$ mlp-create -t shrinked -d data/shrinked20x20/ -s classifier.p
63.3431085044 %  # Success rate on testing data

Extract text from the given image using the classifier we just made. Resize the image to reduce computation time.

$ mlp-extract data/test/sample3.jpg -p classifier.p --resize 0.25
iYYij jY N CjYj iYjV1Y1L 1j Y YYLG1  # Yeah, this isn't very good output

Development

To develop/test other classifiers, you would just need to create a classifier wrapper from ocr.classifiers.base.Classifier and create an extraction script that uses this classifier to get text from an image.

Example usages of this are in the KNN and MLP Classifiers in ocr/classifiers/ and examples of implementing the extraction script are in scripts/.

Results

The package functionally works in that it can create and train classifiers, and use these classifiers to extract text from images, but this package does not work in that the correct text is extracted. The following are ways to improve performance:

• Better sample data
• Better features
  • Only the individual grayscale pixel values are used as featues. Alternative features that should be experimented with include:
    • Number of edges on each character
    • Number of circles on each character
    • Projecting each character onto the x and y axis and using the distributions along each axis as featues.
• Smarter character region finding in an image.

For now, this package works best as a testbed for experimenting with different classifiers and features.

TODO

• Update unit tests.
• Make presentation.