Spaceomitting

Spaceomitting refers to the intentional or incidental removal of spaces between words or tokens in text, data streams, or written systems. It covers a range of practices from typographic styling that excludes spaces to automatic whitespace stripping in data processing. The term is not consistently defined and is used variably across disciplines, sometimes described as whitespace removal or space omission.

Contexts and origins: In languages and scripts that do not separate words with spaces, such as traditional Chinese and Japanese, space omitting is natural. In digital formats, space removal occurs in compression, obfuscation, or microblogging where users shorten text. OCR and hand-typed input may yield space omissions due to errors. Some programming or data encoding schemes deliberately omit spaces to minimize size or avoid tokenization, or in esoteric coding styles.

Implications and methods: Removing spaces affects readability and machine processing. NLP tasks often require reintroducing word boundaries through tokenization, language models, or segmentation algorithms. Conversely, space omission can aid compression or obfuscation but complicates search, indexing, and accessibility. When spaces are omitted, cues such as capitalization, punctuation, and known lexicons are used to infer boundaries.

Example: The string spaceomitting can be read as space omitting or as a single token depending on the context. Similar issues arise with thisisanexample versus this is an example.

See also: whitespace, word segmentation, text normalization, tokenization, data compression, OCR post-processing.