Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.28103 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911701975171072 |
|---|---|
| author | Curini, Luigi Ferrara, Alfio Pagano, Giovanni Picascia, Sergio |
| author_facet | Curini, Luigi Ferrara, Alfio Pagano, Giovanni Picascia, Sergio |
| contents | Parliamentary proceedings represent a rich yet challenging resource for computational analysis, particularly when preserved only as scanned historical documents. Existing efforts to transcribe Italian parliamentary speeches have relied on traditional Optical Character Recognition pipelines, resulting in transcription errors and limited semantic annotation. In this paper, we propose a pipeline based on Vision-Language Models for the automatic transcription, semantic segmentation, and entity linking of Italian parliamentary speeches. The pipeline employs a specialised OCR model to extract text while preserving reading order, followed by a large-scale Vision-Language Model that performs transcription refinement, element classification, and speaker identification by jointly reasoning over visual layout and textual content. Extracted speakers are then linked to the Chamber of Deputies knowledge base through SPARQL queries and a multi-strategy fuzzy matching procedure. Evaluation against an established benchmark demonstrates substantial improvements both in transcription quality and speaker tagging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28103 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Transcription and Recognition of Italian Parliamentary Speeches Using Vision-Language Models Curini, Luigi Ferrara, Alfio Pagano, Giovanni Picascia, Sergio Digital Libraries Artificial Intelligence Information Retrieval Parliamentary proceedings represent a rich yet challenging resource for computational analysis, particularly when preserved only as scanned historical documents. Existing efforts to transcribe Italian parliamentary speeches have relied on traditional Optical Character Recognition pipelines, resulting in transcription errors and limited semantic annotation. In this paper, we propose a pipeline based on Vision-Language Models for the automatic transcription, semantic segmentation, and entity linking of Italian parliamentary speeches. The pipeline employs a specialised OCR model to extract text while preserving reading order, followed by a large-scale Vision-Language Model that performs transcription refinement, element classification, and speaker identification by jointly reasoning over visual layout and textual content. Extracted speakers are then linked to the Chamber of Deputies knowledge base through SPARQL queries and a multi-strategy fuzzy matching procedure. Evaluation against an established benchmark demonstrates substantial improvements both in transcription quality and speaker tagging. |
| title | Transcription and Recognition of Italian Parliamentary Speeches Using Vision-Language Models |
| topic | Digital Libraries Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2603.28103 |