wordsegment

Wordsegment is a lightweight natural language processing utility designed to split strings that contain concatenated words into individual words. It is commonly used to post-process text produced by optical character recognition (OCR), to parse hashtags or user handles, and to improve search indexing by turning contiguous character sequences into tokenizable words.

The project is implemented as a Python library and relies on a precomputed frequency-based dictionary of English words. It uses a dynamic programming algorithm to place spaces so that the resulting sequence of words has the highest probability according to the dictionary. A typical workflow is to load the dictionary with load() and then segment strings with segment('hellothere') to obtain 'hello there'. The frequency data are derived from large text corpora. The library is open source and available on PyPI; installation is commonly done with pip install wordsegment.

Usage details and limitations are important. The segmentation outcome depends on the quality and scope of the underlying word frequency data, which can bias results toward common terms and struggle with rare words, proper names, or highly specialized terminology. It is not a full tokenizer and does not automatically handle punctuation or capitalization beyond basic processing. Wordsegment is best used for scenarios involving English text or for illustrating word boundaries in otherwise unsegmented strings. In practice, it complements other NLP tools that perform comprehensive tokenization and language-specific processing.