A Novel Procedure to Speed up the Transcription of Historical Handwritten Documents by Interleaving Keyword Spotting and user Validation We propose a novel procedure to speedup the content transcription of handwritten documents in digital historical archives when a keyword spotting system is used for the purpose. Instead of performing the validation of the system outputs in a single step, as it is customary, the proposed methodology envisaged a multistep validation process to be embedded into a humanintheloop approach. At each step, the system outputs are validated and, whenever an image word that does not correspond to any entry of the keyword list is mistakenly returned by the system, its correct transcription is entered and used to query the system in the next step. The performance of our approach has been experimentally evaluated in terms of the total time to achieve the complete transcription of a subset of documents from the Bentham dataset. The results confirm that interleaving keyword spotting by the system and validation by the user leads to a significant reduction of the time required to transcribe the document content with respect to both the manual transcription and the traditional endoftheloop validation process.

The Pinkas Dataset In historical document image processing, datasets account for a significant part of any research, and are crucial for the diversity and abundance of experimental results, which contribute to the development of new algorithms to meet the new challenge. Moreover, they are very important for benchmarking processing algorithms. Numerous publicly available document image datasets of different languages have been emerged. However, current segmentation and recognition performances are nearly saturated with respect to the present publicly available datasets. As such, collecting and labelling historical document images is a burden on historical document image processing researchers. This paper introduces a public historical document image dataset, Pinkas dataset, with new challenges to open room for improvement and identify strengths and weaknesses of available processing algorithms. It is the first dataset in medieval handwritten Hebrew and fully labeled at word, line and page level by an expert of historical Hebrew manuscripts. Pinkas dataset contributes to the diversity of benchmarking standards. In this paper we present meta features of Pinkas dataset and apply recent word spotting algorithms to analyze the room for improvement in terms of performance.