This package extracts the name, number and email from the text of business cards.

The code is implemented for python 2.7 and requires no special dependencies.

• parse.py: invokes the package to extract info from sample text filess
• train_classifier.py: estimates the person name classifier from training data.
• BusinessCardParser/: python package implements the solution.
• data/: training data for name classifier and test data for business cards.

How to run

To parse txt files containing business card output,

python parse.py -m data/name.model data/tests/*

To train the name model on different data,

python train_classifier.py -f data/name.model data/train/names.txt

Details

Each line of the business card is scored against three extractors (name, number, email). The extractors are either regexes or simple classifiers.

Email Extraction

A permissive regex is used. It is not RFC-5322 compliant since that standard is extremely flexibile and this application is very simple.

I assume the @ sign is unlikely to often appear in business cards, so ensuring full compliance with the standard is unnecessary at this point.

Phone Number Extraction

A regex first identifies a phone number.

• Only matches U.S. phone numbers (1-111-111-1111)
• Tries to extract extensions if present

If multiple numbers are present, a second pass looks at the whole line for context of a primary phone number. If multiple primary numbers are found, the top-most one is selected.

Positive Indicators

• tel
• telephone
• phone
• office
• work
• p
• number
• direct

Negative Indicators

• fax
• cell
• home
• f
• mobile

Further extensiosn to the code could add:

• non-U.S. phone numbers
• slot filling to populate work/cell/home

Name Extraction

A naive bayes classifier built on character 4-grams is used.

Common first and last names came from http://names.mongabay.com/

A training corpus was then randomly generated from these word lists. From here, 4-gram frequencies were estimated with a small smoothing penalty.

The most likely line unde this model is selected as the name.

Extensions could add:

• a discriminative classifier based on name vs. company
• character sequence models built on a language model or recurrent neural networks
• better training data based on real first and last name pairs
• move code to scipy/numpy. For dependency simplicity, I did not use these libraries.