TextCount

TextCount is a software project that provides tools for counting text elements in strings, files, or streams. It is designed to offer Unicode-aware counting across multiple dimensions, making it useful for developers, researchers, and data scientists who need precise text length measurements for validation, analytics, or preprocessing.

Overview

TextCount focuses on measuring characters, bytes, words, sentences, and tokens. It emphasizes correctness with Unicode text, supporting normalization and grapheme cluster handling to yield predictable results across languages. The project aims to be language-agnostic, with bindings and components that can be integrated into various workflows.

Features

- Counts for characters, bytes, words, sentences, and tokens

- Unicode-aware processing with normalization and grapheme cluster support

- Locale- and rule-aware word boundaries

- Streaming and batch processing modes

- Pluggable tokenizers and bindings for multiple programming languages (such as Python, JavaScript, Java, and Go)

Usage and applications

TextCount is commonly used to enforce input length constraints in APIs and user interfaces, to analyze text datasets for machine learning pipelines, and to validate or compare document lengths in publishing or archival contexts. It can operate on entire documents or on individual segments and can be configured to ignore or include whitespace and punctuation according to the task requirements.

Implementation and design

The core architecture centers on a tokenizer framework that can be extended with custom rules. The implementation favors deterministic outputs and efficient memory use, enabling reliable counting even for large text corpora. The project typically provides a CLI tool as well as library modules that can be incorporated into larger software systems.

See also

Text processing, tokenization, natural language processing, word count.