Illustrated portrait of Emanuela Boros
Emanuela Boros Researcher · NLP & document AI

I study how machines read imperfect traces.

My work is on AI for noisy documents: historical newspapers, OCR and HTR transcripts, handwritten archives, inscriptions, administrative and financial records. I build methods to extract people, places, events and relations from them, and I measure what models miss, invent or flatten along the way.

GitHub Google Scholar ORCID LinkedIn PhD thesis
01 Scan · OCR outputCER 6.1%

La Roche1le, l4 juillet 189O. — Le Conscil municipal s’est réuni à l’Hôtel de Vi11e.

02 Read · named entitiesNER

La Rochelle, 14 juillet 1890. — Le Conseil municipal s’est réuni à l’Hôtel de Ville.

03 Link · knowledge graphEL
From noisy scan to structured evidence: the pipeline my research tries to make robust. Illustrative example.
01 — Research

Six threads I keep returning to

Each thread links to a page with the problem, the approach, and the papers behind it.

01

Noisy documents

OCR and HTR errors, degraded text, and how noise changes downstream meaning, not just character accuracy.

Research + papers →
02

Semantic extraction

Named entities, entity linking, relations and events: the small structures that make collections searchable.

Research + papers →
03

Historical AI

Temporal modelling and multilingual archives, for language whose names, borders and meanings keep shifting.

Research + papers →
04

Cultural heritage

Digital epigraphy, Armenian and Ukrainian inscriptions, structured vocabularies, and contested memory.

Research + papers →
05

Multimodality

Text, images, layout and visual signals modelled together, rather than treating everything as plain text.

Research + papers →
06

Applied document intelligence

Document fraud, misinformation, epidemic surveillance, and open LLMs built to hold up under real constraints.

Research + papers →
02 — Publications

Selected papers

1,180 citations · h-index 17 on Google Scholar. Open a paper below for its challenge and conclusion.

    Noisy documents Semantic extraction Historical AI Cultural heritage Multimodality Applied document intelligence
    All 85 publications by theme and year.
    The same 85 publications by theme.
    The same 85 publications by type.
    Full list: 85 publications →
    03 — Writing

    Notes and essays

    Shorter texts on AI, archives, evidence and noise. Less formal than papers.

    All writing →
    04 — About

    Mostly serious work, with a sense of humour

    I care about what AI extracts from documents, and also about what it misses, invents, flattens, or quietly ruins while sounding confident.

    I come from both computer science and visual practice, so I tend to look at documents as text, images, layouts and evidence all at once. My PhD was on neural methods for event extraction; since then I have worked on historical newspapers, epigraphy, document fraud, epidemic surveillance and open LLMs.

    Outside research: photography, handmade things, gardening with variable success, mostly non-fantasy books, metal festivals and camping.