Open Set Classification of Untranscribed Handwritten Documents

TL;DR

This paper presents an open set classification method for untranscribed handwritten documents using probabilistic indexing, enabling automatic document typology tagging to improve archive organization and accessibility.

Contribution

It introduces a novel application of probabilistic indexing for classifying handwritten documents based on textual content without transcriptions.

Findings

Promising classification accuracy on complex handwritten manuscripts

Effective handling of word-level uncertainty in handwritten text

Improved metadata tagging for large manuscript collections

Abstract

Huge amounts of digital page images of important manuscripts are preserved in archives worldwide. The amounts are so large that it is generally unfeasible for archivists to adequately tag most of the documents with the required metadata so as to low proper organization of the archives and effective exploration by scholars and the general public. The class or ``typology'' of a document is perhaps the most important tag to be included in the metadata. The technical problem is one of automatic classification of documents, each consisting of a set of untranscribed handwritten text images, by the textual contents of the images. The approach considered is based on ``probabilistic indexing'', a relatively novel technology which allows to effectively represent the intrinsic word-level uncertainty exhibited by handwritten text images. We assess the performance of this approach on a large collection of complex notarial manuscripts from the Spanish Archivo Host\'orico Provincial de C\'adiz, with promising results.