scholarly journals Optical Character Recognition using Ant Miner Algorithm: A Case Study on Oriya Character Recognition

2013 ◽  
Vol 61 (3) ◽  
pp. 17-22 ◽  
Author(s):  
Bhagirath Kumar ◽  
Niraj Kumar ◽  
Charulata Palai ◽  
Pradeep Kumar Jena ◽  
Subhagata Chattopadhyay
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Optical Character

Analysis of OCR Design and Implementation for GUI Modeling

2011 ◽  
Vol 128-129 ◽  
pp. 1303-1307
Author(s):  
Yu Mei Wu ◽  
Zhi Fang Liu
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Work Load ◽  
Test Case ◽  
New Approach ◽  
Design And Implementation ◽  
Optical Character ◽  
Automated Test Case Generation ◽  
Model Based Testing

Many efforts have been taken to achieve automated Graphical User Interface (GUI) testing. The most popular way is model-based testing, which supports automated test case generation and execution. But building such a model is a non-trivial task, which usually costs the most work-load in the entire testing process. Most of the approaches about automated model deriving are dependant on the programming language or specific OS. In this paper, we proposed a new approach of GUI modeling using Optical Character Recognition (OCR), and technical poblems encountered have been analyzed in deatail. Case study shows that our approach is capable of analyzing most of the GUI windows, and generating corresponding model and hence eliminates the above constraint.

scholarly journals Quantifying the impact of dirty OCR on historical text analysis: Eighteenth Century Collections Online as a case study

2019 ◽  
Vol 34 (4) ◽  
pp. 825-843 ◽  
Author(s):  
Mark J Hill ◽  
Simon Hengchen
Keyword(s):  
Eighteenth Century ◽  
Text Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Ground Truth ◽  
Optical Character ◽  
Historical Text ◽  
The Impact

Abstract This article aims to quantify the impact optical character recognition (OCR) has on the quantitative analysis of historical documents. Using Eighteenth Century Collections Online as a case study, we first explore and explain the differences between the OCR corpus and its keyed-in counterpart, created by the Text Creation Partnership. We then conduct a series of specific analyses common to the digital humanities: topic modelling, authorship attribution, collocation analysis, and vector space modelling. The article concludes by offering some preliminary thoughts on how these conclusions can be applied to other datasets, by reflecting on the potential for predicting the quality of OCR where no ground-truth exists.

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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