scholarly journals Development of Training Data for Optical Character Recognition using Deformed Printing Characters

2018 ◽  
Author(s):  
Ken Kariya ◽  
Takahiro Fujishima ◽  
Lifeng Zhang
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Training Data ◽  
Optical Character

scholarly journals Backpropagation Neural Network for Book Classification Using the Image Cover

2020 ◽  
Vol 2 (2) ◽  
pp. 89-106
Author(s):  
I Putu Budhi Darma Purwanta ◽  
◽  
Ni Putu Novita Puspa Dewi ◽  
Cyprianus Kuntoro Adi ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Image Resolution ◽  
Training Data ◽  
Optical Character ◽  
Artificial Neural ◽  
Scanned Images ◽  
Hidden Layer ◽  
Quality Variation

Artificial Neural Networks are known to provide a good model for classification. The goal of this research is to classify books in Bahasa (Bahasa Indonesia) using its cover. The data is in the form of scanned images, each with the size of 300 cm height, 130 cm width, and 96 dpi image resolution the research conducted features extraction using image processing method, MSER (Maximally Stable Externally Regions) to identify the area of book title, and Tesseract Optical Character Recognition (OCR) to detect the title. Next, features extracted from MSER and OCR are converted into a numerical matrix as the input to the Backpropagation Artificial Neural Network. The accuracy obtained using one hidden layer and 15 neurons is 63.31%. Meanwhile, the evaluation using 2 hidden layers with a combination of 15 and 35 neurons resulted in accuracy of 79.89%. The ability of the model to classify the book was affected by the image quality, variation, and number of training data.

scholarly journals Optical character recognition with neural networks and post-correction with finite state methods

2020 ◽  
Vol 23 (4) ◽  
pp. 279-295
Author(s):  
Senka Drobac ◽  
Krister Lindén
Keyword(s):  
Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Mixed Model ◽  
Training Data ◽  
Language Models ◽  
High Quality ◽  
Link Type ◽  
Optical Character ◽  
Mixed Language

Abstract The optical character recognition (OCR) quality of the historical part of the Finnish newspaper and journal corpus is rather low for reliable search and scientific research on the OCRed data. The estimated character error rate (CER) of the corpus, achieved with commercial software, is between 8 and 13%. There have been earlier attempts to train high-quality OCR models with open-source software, like Ocropy (https://github.com/tmbdev/ocropy) and Tesseract (https://github.com/tesseract-ocr/tesseract), but so far, none of the methods have managed to successfully train a mixed model that recognizes all of the data in the corpus, which would be essential for an efficient re-OCRing of the corpus. The difficulty lies in the fact that the corpus is printed in the two main languages of Finland (Finnish and Swedish) and in two font families (Blackletter and Antiqua). In this paper, we explore the training of a variety of OCR models with deep neural networks (DNN). First, we find an optimal DNN for our data and, with additional training data, successfully train high-quality mixed-language models. Furthermore, we revisit the effect of confidence voting on the OCR results with different model combinations. Finally, we perform post-correction on the new OCR results and perform error analysis. The results show a significant boost in accuracy, resulting in 1.7% CER on the Finnish and 2.7% CER on the Swedish test set. The greatest accomplishment of the study is the successful training of one mixed language model for the entire corpus and finding a voting setup that further improves the results.

SELECTION TECHNIQUE FOR MULTIPLE OUTPUTS OF OPTICAL CHARACTER RECOGNITION

2020 ◽  
Vol 8 (2) ◽  
pp. 41-51
Author(s):  
I.Q. Habeeb ◽  
Z.Q. Al-Zaydi ◽  
H.N. Abdulkhudhur
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Selection Technique ◽  
Multiple Outputs ◽  
Optical Character

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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