Towards a Higher Accuracy of Optical Character Recognition of Chinese Rare Books in Making Use of Text Model

2019 ◽  
Author(s):  
Hsiang-An Wang ◽  
Pin-Ting Liu
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Optical Character ◽  
Text Model ◽  
Rare Books

scholarly journals Optical character recognition of typeset Coptic text with neural networks

2019 ◽  
Vol 34 (Supplement_1) ◽  
pp. i135-i141
Author(s):  
So Miyagawa ◽  
Kirill Bulert ◽  
Marco Büchler ◽  
Heike Behlmer
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Accuracy Rate ◽  
Optical Character ◽  
The Neural Network ◽  
Text Model ◽  
The Many

Abstract Digital Humanities (DH) within Coptic Studies, an emerging field of development, will be much aided by the digitization of large quantities of typeset Coptic texts. Until recently, the only Optical Character Recognition (OCR) analysis of printed Coptic texts had been executed by Moheb S. Mekhaiel, who used the Tesseract program to create a text model for liturgical books in the Bohairic dialect of Coptic. However, this model is not suitable for the many scholarly editions of texts in the Sahidic dialect of Coptic which use noticeably different fonts. In the current study, DH and Coptological projects based in Göttingen, Germany, collaborated to develop a new Coptic OCR pipeline suitable for use with all Coptic dialects. The objective of the study was to generate a model which can facilitate digital Coptic Studies and produce Coptic corpora from existing printed texts. First, we compared the two available OCR programs that can recognize Coptic: Tesseract and Ocropy. The results indicated that the neural network model, i.e. Ocropy, performed better at recognizing the letters with supralinear strokes that characterize the published Sahidic texts. After training Ocropy for Coptic using artificial neural networks, the team achieved an accuracy rate of >91% for the OCR analysis of Coptic typeset. We subsequently compared the efficiency of Ocropy to that of manual transcribing and concluded that the use of Ocropy to extract Coptic from digital images of printed texts is highly beneficial to Coptic DH.

A Structured Method for the Recognition of Complex Historical Tables

1997 ◽  
Vol 9 (1-3) ◽  
pp. 58-77
Author(s):  
Vitaly Kliatskine ◽  
Eugene Shchepin ◽  
Gunnar Thorvaldsen ◽  
Konstantin Zingerman ◽  
Valery Lazarev
Keyword(s):  
Nineteenth Century ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Complex Structure ◽  
Source Material ◽  
Historical Sources ◽  
Tax Assessment ◽  
Optical Character ◽  
Algorithmic Model ◽  
Machine Readable

In principle, printed source material should be made machine-readable with systems for Optical Character Recognition, rather than being typed once more. Offthe-shelf commercial OCR programs tend, however, to be inadequate for lists with a complex layout. The tax assessment lists that assess most nineteenth century farms in Norway, constitute one example among a series of valuable sources which can only be interpreted successfully with specially designed OCR software. This paper considers the problems involved in the recognition of material with a complex table structure, outlining a new algorithmic model based on ‘linked hierarchies’. Within the scope of this model, a variety of tables and layouts can be described and recognized. The ‘linked hierarchies’ model has been implemented in the ‘CRIPT’ OCR software system, which successfully reads tables with a complex structure from several different historical sources.

CNN-based Rain Reduction in Street View Images

2020 ◽  
Vol 2020 (1) ◽  
pp. 78-81
Author(s):  
Simone Zini ◽  
Simone Bianco ◽  
Raimondo Schettini
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Weather Conditions ◽  
Specific Interest ◽  
Optical Character ◽  
Street View ◽  
In The Wild ◽  
Bad Weather ◽  
Detection And Recognition

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.

Pengantar dan Survey Tentang Optical Music Recognition

2014 ◽  
Vol 6 (1) ◽  
pp. 36-39
Author(s):  
Kevin Purwito
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Optical Music Recognition ◽  
Optical Character ◽  
Digital Format ◽  
Music Recognition ◽  
Index Terms ◽  
The Many ◽  
Further Development ◽  
Music Symbol

This paper describes about one of the many extension of Optical Character Recognition (OCR), that is Optical Music Recognition (OMR). OMR is used to recognize musical sheets into digital format, such as MIDI or MusicXML. There are many musical symbols that usually used in musical sheets and therefore needs to be recognized by OMR, such as staff; treble, bass, alto and tenor clef; sharp, flat and natural; beams, staccato, staccatissimo, dynamic, tenuto, marcato, stopped note, harmonic and fermata; notes; rests; ties and slurs; and also mordent and turn. OMR usually has four main processes, namely Preprocessing, Music Symbol Recognition, Musical Notation Reconstruction and Final Representation Construction. Each of those four main processes uses different methods and algorithms and each of those processes still needs further development and research. There are already many application that uses OMR to date, but none gives the perfect result. Therefore, besides the development and research for each OMR process, there is also a need to a development and research for combined recognizer, that combines the results from different OMR application to increase the final result’s accuracy. Index Terms—Music, optical character recognition, optical music recognition, musical symbol, image processing, combined recognizer  

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