Assessing the quality of digital re-publishing of textual documents through the follow-up of a correction protocol by crowdsourcing

Abstract

Digitized re-publishing of documents has become nowadays a very important issue. Optical Character Recognition (OCR) has been intensively used to this aim, as it performs the transcription of the text images into electronic files, allowing display functionalities, indexation, enrichment and broadcasting. However, such software still fails in many configurations, so that the transcription does not reach the required editorial quality (99% of recognition are required for an ergonomic reading). In the OZALID project, we propose to rely on crowdsourcing for correcting OCR results. One main issue is then to determine when the crowdsourcing has reached its limits. For that, we present a feasibility study of an original protocol based on indicators that quantify the recognition quality in both semantic and semiotic ways. These indicators are calculated and followed up during the entire crowdsourcing process until stability. Experimental results show that the proposed observables converge after some correction iterations allowing automatically stopping the crowdsourcing process and dealing with huge amount of data.

Information Gradient among Nucleotide Sequences of Essential RNAs in an Evolutionary Perspective

“…In [52,53], we have used a new classifier reversible (able to make explicit and give explanation on its reasoning at all the steps of clustering) and agnostic (not using an a priori semantic knowledge on the data) called Maxwell © . On Figure 4, we see the PpAL for the whole genomes of Methanomada Archaea clusterized by the classifier Maxwell © [53] and a phylogenetic tree [54] with indication of the PpAL (in red) respecting the phylogeny ranking.…”