# Inscriptis -- A Python-based HTML to text conversion library optimized for knowledge extraction from the Web

arXiv: https://arxiv.org/abs/2108.01454 · HTML: https://mlsanity.com/paper/2108.01454/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2021.

## Built on by (most cited first, 6 of 6)

- [The Massive Legal Embedding Benchmark (MLEB)](https://mlsanity.com/paper/2510.19365/) (2025)
- [Cleaner Pretraining Corpus Curation with Neural Web Scraping](https://mlsanity.com/paper/2402.14652/) (2024)
- [AXE: Low-Cost Cross-Domain Web Structured Information Extraction](https://mlsanity.com/paper/2602.01838/) (2026)
- [From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence](https://mlsanity.com/paper/2605.06006/) (2026)
- [Scoring the Unscorables: Cyber Risk Assessment Beyond Internet Scans](https://mlsanity.com/paper/2506.06604/) (2025)
- [Cross-Domain Web Information Extraction at Pinterest](https://mlsanity.com/paper/2508.01096/) (2025)

## Abstract (arXiv)

Inscriptis provides a library, command line client and Web service for converting HTML to plain text. Its development has been triggered by the need to obtain accurate text representations for knowledge extraction tasks that preserve the spatial alignment of text without drawing upon heavyweight, browser-based solutions such as Selenium. In contrast to related software packages, Inscriptis (i) provides a layout-aware conversion of HTML that more closely resembles the rendering obtained from standard Web browsers; and (ii) supports annotation rules, i.e., user-provided mappings that allow for annotating the extracted text based on structural and semantic information encoded in HTML tags and attributes. These unique features ensure that downstream knowledge extraction components can operate on accurate text representations, and may even use information on the semantics and structure of the original HTML document.