Word Spotting in Historical Document Collections with Online-Handwritten Queries

2016 ◽  
Author(s):  
Christian Wieprecht ◽  
Leonard Rothacker ◽  
Gernot A. Fink
Keyword(s):  
Historical Document ◽  
Document Collections ◽  
Word Spotting

Efficient segmentation-free keyword spotting in historical document collections

2015 ◽  
Vol 48 (2) ◽  
pp. 545-555 ◽  
Author(s):  
Marçal Rusiñol ◽  
David Aldavert ◽  
Ricardo Toledo ◽  
Josep Lladós
Keyword(s):  
Keyword Spotting ◽  
Historical Document ◽  
Document Collections

Towards information retrieval on historical document collections: the role of matching procedures and special lexica

2010 ◽  
Vol 14 (2) ◽  
pp. 159-171 ◽  
Author(s):  
Annette Gotscharek ◽  
Ulrich Reffle ◽  
Christoph Ringlstetter ◽  
Klaus U. Schulz ◽  
Andreas Neumann
Keyword(s):  
Information Retrieval ◽  
Historical Document ◽  
Document Collections

Keyword spotting in historical document collections withoutsegmentation using the Siamese Network

2021 ◽  
Author(s):  
Ashwini Sapkal ◽  
Chhavi ◽  
Shashank Sharma ◽  
Pradeep Kumar ◽  
Sachin Yadav
Keyword(s):  
Keyword Spotting ◽  
Historical Document ◽  
Document Collections ◽  
Siamese Network

The Incremental Artificial Immune System for Arabic Handwritten Recognition

2019 ◽  
Vol 12 (4) ◽  
pp. 88-106
Author(s):  
Khelil Hiba ◽  
Benyettou Abdelkader ◽  
Afef Kacem
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Handwriting Recognition ◽  
Document Analysis ◽  
National Archive ◽  
Historical Document ◽  
Artificial Immune ◽  
Word Spotting ◽  
Frequent Use ◽  
Handwritten Recognition

The historical document is a treasure. The frequent use of these documents requires having a numeric copy. The use of these numeric documents requires developing techniques to facilitate their use. The search by content, the word spotting, and handwriting recognition became important points of research in document analysis. For this purpose, in this article is covered the recognition of the Arabic manuscript names extracted from the register of names of the Tunisian national archive. In the study, the authors have used several techniques for extracting knowledge, coding, and name recognition. The authors have also optimized the clonclas algorithm using the incremental principle from the i2gng algorithm. The results encourage continuing exploration.

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