Sentencebounding

Sentencebounding is the task of identifying and localizing sentences within text content, either in plain text as sentence spans or in document images as bounding boxes. It supports sentence-level processing, indexing, translation, and retrieval in natural language processing and document image analysis.

In plain text applications, sentencebounding is often called sentence boundary detection or sentence segmentation. It relies on punctuation, capitalization, and other cues, and may use rule-based heuristics or statistical and neural models, with transformers offering improved handling of tricky cases such as abbreviations and multilingual text.

In document image analysis, sentencebounding means producing coordinates for sentences within a page image, enabling sentence-level recognition, layout understanding, and downstream tasks such as translation and indexing. Techniques combine text detection with layout reasoning, using bounding-box regression, region proposal methods, or end-to-end detectors guided by OCR outputs.

Key challenges include abbreviated forms, hyphenation across lines, mixed languages, curved baselines, complex layouts, and OCR errors, all of which can cause missed or merged sentence boundaries and imprecise bounding boxes.

Applications span digitization of archives, searchable corpora, multilingual translation, information extraction, and academic text analysis. Evaluation typically uses intersection-over-union between predicted and ground-truth sentence boxes, along with precision, recall, and F1 for boundary accuracy.

See also sentence boundary detection, OCR, document layout analysis.