LocalRedact vs Philter

LocalRedact is a free, open-source PDF redaction tool that follows plain-English instructions using deterministic rules. It removes text beneath redacted areas and blanks image pixels where those areas overlap. Detection can target personal, financial, government, health, and credential data, as well as quoted values and selected page or layout instructions. Processing happens locally: the project says it uses no LLM, cloud API, telemetry, or model download, and reads text and coordinates with PyMuPDF. By default, it removes common PDF extras such as metadata, embedded files, JavaScript, links, and saved form state. After saving, it reopens the document and checks whether detected values remain extractable. Interfaces include a Python API, two command-line tools, and an optional HTTP service. Scanned PDFs can use locally installed Tesseract; image-only pages fail closed without OCR by default. The package requires Python 3.10 or later and is available for Windows, macOS, and Linux. The project calls the software alpha and warns that detection is best effort, not a compliance certification. It recommends visually reviewing high-stakes output.

Philter is open-source software for finding and redacting personally identifiable information and protected health information in text. For detected entities, users can choose to mask, encrypt, replace with synthetic values, remove, or pass them through. Policies, custom dictionaries, and identifier patterns such as medical record numbers and transaction IDs let teams configure what to detect and how to handle it. General Purpose, Healthcare, and COVID-19 NLP lenses address different types of text. Philter provides an HTTP REST API, a Java SDK, and an OpenAPI specification for generating clients in other languages. It can run in a private environment or through AWS, Google Cloud, and Microsoft Azure marketplaces. The maker says text stays in the customer’s environment, including firewalled or air-gapped deployments. Redaction requests are recorded in a tamper-evident, hash-chained audit log that can be queried and exported to CSV. Detection is probabilistic, so results should be checked against an organization’s own data before production use. The open-source toolkit has community support; commercial licenses include direct support.