Many users struggle with unpunctuated text, making their writing hard to read. This often happens when people type quickly, are non-native speakers, or prioritize speed over formatting. While autocorrect tools fix spelling and grammar, they rarely address missing punctuation—a gap that affects clarity in texts, emails, and notes.
One approach could involve upgrading autocorrect systems to suggest punctuation in real time, similar to how they highlight misspelled words. Natural language processing (NLP) could identify where commas, periods, or question marks belong in unpunctuated text. For example, if someone types "lets go to the park its sunny," the system might propose adding an apostrophe in "let's" and a period after "park." Suggestions could appear as lightweight, tappable prompts to avoid disrupting typing flow.
This could be built as a feature for keyboard apps or directly into operating systems. Early versions might focus on basic punctuation, while later iterations could learn user preferences (e.g., favoring em dashes or semicolons) or adapt to different languages. To test viability, a simple prototype could analyze typed notes and measure how often users accept its suggestions.
Unlike grammar checkers that require separate edits, this idea would make punctuation help feel like a natural part of typing—potentially improving communication with minimal effort.
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