학술논문

Open Set Classification of Untranscribed Handwritten Documents
Document Type
Working Paper
Source
Subject
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Computation and Language
Computer Science - Information Retrieval
Computer Science - Machine Learning
Language
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.