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Callico: a Versatile Open-Source Document Image Annotation Platform

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arxiv 2405.01071 v1 pith:QODMGZNC submitted 2024-05-02 cs.CV cs.DL

classification cs.CVcs.DL
keywords annotationcallicodocumentplatformdatahigh-qualityimagelearning
verification ladder T0 review T1 audit T2 compute T3 formal

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This paper presents Callico, a web-based open source platform designed to simplify the annotation process in document recognition projects. The move towards data-centric AI in machine learning and deep learning underscores the importance of high-quality data, and the need for specialised tools that increase the efficiency and effectiveness of generating such data. For document image annotation, Callico offers dual-display annotation for digitised documents, enabling simultaneous visualisation and annotation of scanned images and text. This capability is critical for OCR and HTR model training, document layout analysis, named entity recognition, form-based key value annotation or hierarchical structure annotation with element grouping. The platform supports collaborative annotation with versatile features backed by a commitment to open source development, high-quality code standards and easy deployment via Docker. Illustrative use cases - including the transcription of the Belfort municipal registers, the indexing of French World War II prisoners for the ICRC, and the extraction of personal information from the Socface project's census lists - demonstrate Callico's applicability and utility.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral

    cs.SE 2025-05 conditional novelty 6.0 of 10

    A new document annotation platform with a human-in-the-spiral workflow claims at least a 41% reduction in annotation time and improved extraction models across iterations.

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