Callico: a Versatile Open-Source Document Image Annotation Platform

TL;DR

Callico is an open-source, web-based platform that streamlines document image annotation for machine learning, supporting various annotation types and collaborative workflows to improve data quality in document recognition tasks.

Contribution

It introduces a versatile, open-source annotation platform with dual-display support and collaborative features tailored for diverse document recognition applications.

Findings

Supports multiple annotation types including layout and key-value annotations.

Enables collaborative annotation workflows.

Proven effective in real-world projects like municipal records and census data.

Abstract

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.