An Interactive Approach with Off-Line and On-Line Handwritten Text Recognition Combination for Transcribing Historical Documents

2016 ◽  
Author(s):  
Emilio Granell ◽  
Veronica Romero ◽  
Carlos D. Martinez-Hinarejos
Keyword(s):  
Text Recognition ◽  
Historical Documents ◽  
Interactive Approach ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
On Line

scholarly journals CNN-Based Page Segmentation and Object Classification for Counting Population in Ottoman Archival Documentation

2020 ◽  
Vol 6 (5) ◽  
pp. 32 ◽  
Author(s):  
Yekta Said Can ◽  
M. Erdem Kabadayı
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Text Recognition ◽  
Historical Documents ◽  
Layout Analysis ◽  
Page Segmentation ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Different Types ◽  
Archival Documentation

Historical document analysis systems gain importance with the increasing efforts in the digitalization of archives. Page segmentation and layout analysis are crucial steps for such systems. Errors in these steps will affect the outcome of handwritten text recognition and Optical Character Recognition (OCR) methods, which increase the importance of the page segmentation and layout analysis. Degradation of documents, digitization errors, and varying layout styles are the issues that complicate the segmentation of historical documents. The properties of Arabic scripts such as connected letters, ligatures, diacritics, and different writing styles make it even more challenging to process Arabic script historical documents. In this study, we developed an automatic system for counting registered individuals and assigning them to populated places by using a CNN-based architecture. To evaluate the performance of our system, we created a labeled dataset of registers obtained from the first wave of population registers of the Ottoman Empire held between the 1840s and 1860s. We achieved promising results for classifying different types of objects and counting the individuals and assigning them to populated places.

A set of benchmarks for Handwritten Text Recognition on historical documents

2019 ◽  
Vol 94 ◽  
pp. 122-134 ◽  
Author(s):  
Joan Andreu Sánchez ◽  
Verónica Romero ◽  
Alejandro H. Toselli ◽  
Mauricio Villegas ◽  
Enrique Vidal
Keyword(s):  
Text Recognition ◽  
Historical Documents ◽  
Handwritten Text ◽  
Handwritten Text Recognition

Turns of the centuries. The Transkribus automated tool for recognition, transcription and translation of handwritten historical documents

Babel ◽  
2020 ◽  
Vol 66 (2) ◽  
pp. 294-310
Author(s):  
Miodrag M. Vukčević
Keyword(s):  
Language Change ◽  
Text Recognition ◽  
Historical Documents ◽  
Writing Style ◽  
Handwritten Documents ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Automated Processing ◽  
Automated Tool

Abstract The translation of handwritten historical documents faces many challenges due to variation in the writing style, local language, and an inevitable language change. Even the transliteration from Cyrillic to Latin characters is standardized by the bijective transliteration standard ISO 9. This presentation introduces a number of tools offered by Transkribus for the automated processing of documents, such as Handwritten Text Recognition (HTR) and Document Understanding, which are needed for the translation of historical documents. Next to the problem of decoding handwritten documents, written for example in Kurrentschrift using ancient terminology, changed meanings and different spelling have additionally to be considered during the translation of texts from earlier centuries. Resolution strategies on a case study show different methods for ensuring quality translations.

scholarly journals Handwritten Text Recognition using Deep Learning with TensorFlow

2020 ◽  
Vol V9 (05) ◽  
Author(s):  
Sri. Yugandhar Manchala ◽  
Jayaram Kinthali ◽  
Kowshik Kotha ◽  
Kanithi Santosh Kumar, Jagilinki Jayalaxmi ◽  
Keyword(s):  
Deep Learning ◽  
Text Recognition ◽  
Handwritten Text ◽  
Handwritten Text Recognition
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