WHITE has sophisticated methods to break down the characters given in Medical context into a set of features (e.g., curves, intersections, loops) that are easier to recognize. The system looks at these features and compares them to stored patterns to determine the most likely match. This approach is more robust to variations in font processing.

WHITE NLP uses the blend of TF-IDF (Term Frequency-Inverse Document Frequency) and CTRank algorithm to valuate the importance of words in a sentence relative to the rest of the document and calculate the centrality of sentences within the document. Cluster similar sentences and then select a representative sentence from each cluster, ensuring accuracy and diversity in the given summary

WHITE uses unsupervised learning techniques to identify the main topics or themes in large bodies of text. In the context of medical summarization, topic modeling can help identify the central issues in a clinical document (e.g., a patient's diagnosis, treatment plan, or follow-up care).

WHITE being an advanced OCR system uses context correctionto fix misrecognized characters and make sense of unusual or complex formatting If the original document includes formatting (e.g., bold, italic, bullet points, tables), WHITE preserves and replicates this formatting in the output.