OCR Post Correction for Endangered Language Texts
There is little to no data available to build natural language processing models for most endangered languages. However, textual data in these languages often exists in formats that are not machine-readable, such as paper books and scanned images. In this work, we address the task of extracting text from these resources. We create a benchmark dataset of transcriptions for scanned books in three critically endangered languages and present a systematic analysis of how general-purpose OCR tools are not robust to the data-scarce setting of endangered languages. We develop an OCR post-correction method tailored to ease training in this data-scarce setting, reducing the recognition error rate by 34% on average across the three languages.
Code (2)
Tasks
Optical Character Recognition (OCR)Similar Papers 제목 키워드 기반
Hybrid Neural-LLM Pipeline for Morphological Glossing in Endangered Language Documentation: A Case Study of Jungar Tuvan
Interlinear glossed text (IGT) creation remains a major bottleneck in linguistic documentation and fieldwork, particularly for low-resource morphologically rich languages. We present a hybrid automatic glossing pipeline …
Structured PredictionLexically Aware Semi-Supervised Learning for OCR Post-Correction
Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Optical character recognition (OCR) can be used to produce digitized text, and previous work has de…
Language ModellingOptical Character RecognitionOptical Character Recognition (OCR)Recovering Text from Endangered Languages Corrupted PDF documents
In this paper we present an approach to efficiently recover texts from corrupted documents of endangered languages. Textual resources for such languages are scarce, and sometimes the few available resources are corrupted…
Using LARA to create image-based and phonetically annotated multimodal texts for endangered languages
We describe recent extensions to the open source Learning And Reading Assistant (LARA) supporting image-based and phonetically annotated texts. We motivate the utility of these extensions both in general and specifically…
RelationEvaluating OpenAI GPT Models for Translation of Endangered Uralic Languages: A Comparison of Reasoning and Non-Reasoning Architectures
The evaluation of Large Language Models (LLMs) for translation tasks has primarily focused on high-resource languages, leaving a significant gap in understanding their performance on low-resource and endangered languages…