Developing a Mixed-Methods Pipeline for Community-Oriented Digitization of Kwak'wala Legacy Texts

TL;DR

This paper presents a mixed-methods pipeline utilizing advanced OCR, language identification, and post-correction techniques to digitize and transcribe historical Kwak'wala texts, aiding language revitalization efforts.

Contribution

It introduces a novel combination of OCR and language processing methods tailored for Indigenous language texts with limited digital resources.

Findings

Effective OCR adaptation for Kwak'wala texts

High-quality transcriptions achieved through combined methods

Facilitates language revitalization and digital archiving

Abstract

Kwak'wala is an Indigenous language spoken in British Columbia, with a rich legacy of published documentation spanning more than a century, and an active community of speakers, teachers, and learners engaged in language revitalization. Over 11 volumes of the earliest texts created during the collaboration between Franz Boas and George Hunt have been scanned but remain unreadable by machines. Complete digitization through optical character recognition has the potential to facilitate transliteration into modern orthographies and the creation of other language technologies. In this paper, we apply the latest OCR techniques to a series of Kwak'wala texts only accessible as images, and discuss the challenges and unique adaptations necessary to make such technologies work for these real-world texts. Building on previous methods, we propose using a mix of off-the-shelf OCR methods, language identification, and masking to effectively isolate Kwak'wala text, along with post-correction models, to produce a final high-quality transcription.