Improving Accessibility of Archived Raster Dictionaries of Complex Script Languages

TL;DR

This paper introduces a web-based system for indexing and accessing raster images of dictionary pages, enhancing accessibility through crowdsourcing, annotations, and multi-language support, significantly reducing manual effort.

Contribution

It presents a novel approach and a web application that enables efficient indexing, searching, and crowd-assisted annotation of raster dictionary images for complex and simple scripts.

Findings

Indexing a 1,000-page dictionary takes less than an hour.

Supports multiple languages and dictionaries simultaneously.

Improves accessibility through feedback and crowdsourcing features.

Abstract

We propose an approach to index raster images of dictionary pages which in turn would require very little manual effort to enable direct access to the appropriate pages of the dictionary for lookup. Accessibility is further improved by feedback and crowdsourcing that enables highlighting of the specific location on the page where the lookup word is found, annotation, digitization, and fielded searching. This approach is equally applicable on simple scripts as well as complex writing systems. Using our proposed approach, we have built a Web application called "Dictionary Explorer" which supports word indexes in various languages and every language can have multiple dictionaries associated with it. Word lookup gives direct access to appropriate pages of all the dictionaries of that language simultaneously. The application has exploration features like searching, pagination, and navigating the word index through a tree-like interface. The application also supports feedback, annotation, and digitization features. Apart from the scanned images, "Dictionary Explorer" aggregates results from various sources and user contributions in Unicode. We have evaluated the time required for indexing dictionaries of different sizes and complexities in the Urdu language and examined various trade-offs in our implementation. Using our approach, a single person can make a dictionary of 1,000 pages searchable in less than an hour.