13th IAPR International Workshop on Document Analysis Systems, DAS 2018, Vienna, Austria, April 24-27, 2018

With Document Image Analysis gaining a strong foothold in the domain of paleography and the emergence of Human-Document Interaction as a narrative, we effectively need to reimagine the way scholars in humanities currently interact with digitized manuscripts. We initially evaluate related work in the current context of post-WIMP interfaces and then introduce our proposed system AMAP. With a strong focus on manuscript exploration, it attempts to harness the current state-of-the-art interaction paradigms to develop an intuitive system to engage with the manuscripts. Keywords-digital paleography; human-document interaction; human-computer interaction; interface design; intelligent interfaces;

[1]  Antonella Fresa,et al.  The Digitization Age: Mass Culture Is Quality Culture. Challenges for Cultural Heritage and Society , 2014, EuroMed.

[2]  Long Jiang,et al.  Ensemble classifier with dividing training scheme for Chinese scene character recognition , 2017, 2017 International Conference on Image and Vision Computing New Zealand (IVCNZ).

[3]  Erez Lieberman Aiden,et al.  Quantitative Analysis of Culture Using Millions of Digitized Books , 2010, Science.

[4]  Alejandro Héctor Toselli,et al.  Multimodal Computer-Assisted transcription of Text Images at Character-Level Interaction , 2012, Int. J. Pattern Recognit. Artif. Intell..

[5]  Claudio De Stefano,et al.  Assisted Transcription of Historical Documents by Keyword Spotting: A Performance Model , 2017, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR).

[6]  Alex Graves,et al.  Supervised Sequence Labelling with Recurrent Neural Networks , 2012, Studies in Computational Intelligence.

[7]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[8]  Kai Chen,et al.  A CNN Based Scene Chinese Text Recognition Algorithm With Synthetic Data Engine , 2016, ArXiv.

[9]  Alicia Fornés,et al.  Divide and conquer: atomizing and parallelizing a task in a mobile crowdsourcing platform , 2013, CrowdMM '13.

[10]  Alicia Fornés,et al.  A bimodal crowdsourcing platform for demographic historical manuscripts , 2014, DATeCH '14.

[11]  Alicia Fornés,et al.  ICDAR2017 Competition on Information Extraction in Historical Handwritten Records , 2017, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR).

[12]  Andrew Zisserman,et al.  Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition , 2014, ArXiv.

[13]  Juho Hamari,et al.  Gamification in Crowdsourcing: A Review , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[14]  Andrew L. Maas Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[15]  Alicia Fornés,et al.  A Tale of Two Transcriptions Machine-Assisted Transcription of Historical Sources , 2015 .