Exploiting Collection Level for Improving Assisted Handwritten Word Transcription of Historical Documents
Transcription of handwritten words in historical documents is still a difficult task. When processing huge amount of pages, document-centered approaches are limited by the trade-off between automatic recognition errors and the tedious aspect of human user annotation work. In this article, we investigate the use of inter page dependencies to overcome those limitations. For this, we propose a new architecture that allows the exploitation of handwritten word redundancies over pages by considering documents from a higher point of view, namely the collection level. The experiments we conducted on handwritten word transcription show promising results in terms of recognition error and human user work reductions.