Most common name parsers use crude pattern matching and the sequence of strings, e.g., the last word is the last name, to parse names. This approach is limited and fragile, especially for Indian names. We take a machine-learning approach to the problem. Using the large voter registration data in India and the US, we build machine-learning-based name parsers that predict whether the string is a first or last name.

For Indian electoral rolls, we assume the last name is the word in the name that is shared by multiple family members. (We table the expansion to include compound last names---extremely rare in India---till the next iteration.)

parsernaam on HF

pip install parsernaam

import pandas as pd
from parsernaam.parse import ParseNames

# Create DataFrame with names to parse
df = pd.DataFrame({'name': ['Jan', 'Nicholas Turner', 'Petersen', 'Nichols Richard', 'Piet',
                           'John Smith', 'Janssen', 'Kim Yeon']})

# Parse names using ML models
results = ParseNames.parse(df)
print(results.to_markdown())

Output:

|    | name            | parsed_name                                                                   |
|---:|:----------------|:------------------------------------------------------------------------------|
|  0 | Jan             | {'name': 'Jan', 'type': 'first', 'prob': 0.677}                            |
|  1 | Nicholas Turner | {'name': 'Nicholas Turner', 'type': 'first_last', 'prob': 0.999}           |
|  2 | Petersen        | {'name': 'Petersen', 'type': 'last', 'prob': 0.534}                        |
|  3 | Nichols Richard | {'name': 'Nichols Richard', 'type': 'last_first', 'prob': 0.999}           |
|  4 | Piet            | {'name': 'Piet', 'type': 'first', 'prob': 0.538}                           |
|  5 | John Smith      | {'name': 'John Smith', 'type': 'first_last', 'prob': 0.997}                |
|  6 | Janssen         | {'name': 'Janssen', 'type': 'first', 'prob': 0.593}                        |
|  7 | Kim Yeon        | {'name': 'Kim Yeon', 'type': 'last_first', 'prob': 0.999}                  |

parse_names input.csv -o output.csv -n name_column

• Machine Learning Based: Uses LSTM neural networks trained on voter registration data
• Multi-language Support: Handles Indian, Western, and other international name patterns
• High Accuracy: Confidence scores provided for each prediction
• Performance Optimized: Model caching and batch processing support
• Robust Error Handling: Handles edge cases like empty names, special characters, etc.

The model is trained on names from the Florida Voter Registration Data from early 2022. The data are available on the Harvard Dataverse

Rajashekar Chintalapati and Gaurav Sood

Contributions are welcome. Please open an issue if you find a bug or have a feature request.

• appeler/naamkaran — generative model for names
• appeler/ethnicolr2 — Ethnicolr implementation with new models in pytorch
• appeler/namesexdata — Data on international first names and sex of people with that name
• appeler/pranaam — pranaam: predict religion based on name
• appeler/graphic_names — Infer the gender of a person with a particular first name using Google image search and Clarifai

The package is released under the MIT License.