Natural Language Processing and Multimedia
A technical view of using NLP to classify, extract, search and fuse OCR/ASR output with visual and acoustic representations.
Multimedia systems may begin with images and audio, yet many applications ultimately produce text. OCR converts visual text and ASR converts speech into text; natural language processing makes those outputs searchable, classifiable and structurally useful.
Noisy text
OCR and ASR outputs contain errors. Character and word mistakes can propagate into entity extraction, search and summarisation. Keeping raw hypotheses, corrected text and confidence values separately preserves traceability.
Token-level timestamps also link text back to the corresponding interval in the original audio or video.
Representation and information extraction
TF-IDF provides sparse and explainable text representations. Dense embeddings support semantic similarity. Transformer encoders provide contextual representations for classification, named-entity recognition and relation extraction.
Natural language processing turns OCR and ASR output from plain text into structured information.
Search and multimodal representation
Inverted indices support exact lexical retrieval, while vector indices support semantic retrieval. Combining both provides complementary failure modes.
Multimodal learning can align text with image objects, faces, speakers and acoustic events on a shared timeline. Keeping source text, derived summaries and model interpretation separate preserves system traceability.
