What is the difference between vision-level and language-level redaction?

Vision-level redaction (or vision token masking) and language-level redaction (or NLP post-processing) are two distinct methods for protecting sensitive information, such as Protected Health Information (PHI), during document processing. They differ primarily in when they operate during the OCR pipeline and what type of information they are best at hiding.

1. Point of Intervention

• Vision-level redaction operates on the image before text is generated. It intercepts the OCR process at the visual encoding stage, taking the bounding boxes of sensitive regions in a document image and replacing those visual patches with learnable "mask tokens". This prevents the sensitive data from ever being embedded into the neural network's dense vector representations and memory.

• Language-level redaction operates on the text after it has been extracted. It applies Natural Language Processing (NLP) techniques—such as regular expressions, named entity recognition (NER), and machine learning classifiers—to identify and redact sensitive information from the plaintext output. The drawback is that the sensitive data has already been fully exposed in the system's memory and intermediate processing stages before the redaction occurs.

2. Effectiveness on Different Data Types

• Vision-level redaction is highly effective for long-form, spatially distributed identifiers. It successfully suppresses identifiers that span large physical areas on the page, such as patient names, dates of birth, and physical addresses. Because these items cover many pixels and visual patches, masking them leaves the language model without enough visual information to guess what was written there.

• Language-level redaction is required for short, structured identifiers. Vision-level masking consistently fails to hide compact, structured data like Social Security Numbers (SSNs), Medical Record Numbers (MRNs), email addresses, and account numbers. This is because the language model decoder uses "contextual inference". If it reads unmasked surrounding text like "Medical Record Number: ___", the model's learned linguistic patterns allow it to confidently predict and generate a plausible number, even if the actual visual patch is completely masked. Language-level redaction easily catches and removes these generated identifiers using strict pattern matching.

Because of these complementary strengths and weaknesses, we suggest a hybrid "defense-in-depth" architecture: vision masking is used first to prevent unpredictable long-form data from being encoded, followed by language-level redaction to clean up any structured identifiers that the language model contextually inferred.