Scribble-Scrabble Genius

2019 ◽  
pp. 37-73
Author(s):  
Yohei Igarashi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Writing System ◽  
Optical Character ◽  
History Of ◽  
Recognition Software

Although Coleridge is mostly known for being a copious talker who was impossible to transcribe, this chapter recovers Coleridge’s role as transcriber, theorist of transcription practices, and inventor of his own idiosyncratic shorthand. Considering Coleridge’s time as a parliamentary reporter, his self-reflexive notebook entries, and the history of stenography, this chapter posits that Coleridge pursued an efficient writing system to record not speech but the flow of his own silent thoughts. Also discussing today’s optical character recognition software and the shorthand effect (when letters or words uncannily become illegible shapes, and non-linguistic shapes come to look like linguistic signs), this chapter culminates in a reading of the “signs” in “The Rime of the Ancient Mariner.”

Arabic optical character recognition software: A review

2017 ◽  
Vol 27 (4) ◽  
pp. 763-776 ◽  
Author(s):  
Faisal Alkhateeb ◽  
Iyad Abu Doush ◽  
Abdelraoaf Albsoul
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Optical Character ◽  
Recognition Software

scholarly journals Utilizing Optical Character Recognition and Boarder Detection Algorithms to Identify Trading Cards

10.29007/qkhd ◽  
2019 ◽  
Author(s):  
Brodie Boldt ◽  
Christopher Cooper ◽  
Ryan Fox ◽  
Jared Parks ◽  
Erin Keith
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Card Game ◽  
Detection Algorithms ◽  
Digital Database ◽  
Optical Character ◽  
The World ◽  
Recognition Software ◽  
Protective Case

Magic: The Gathering is a popular physical trading card game played by millions of people around the world. To keep track of their cards, players typically store them in some sort of physical protective case, which can become cumbersome to sort through as the number of cards can reach up to the thousands. By utilizing and improving optical character recognition software, the TCG Digitizer allows users to efficiently store their entire inventory of Magic: The Gathering trading cards in a digital database. With an emphasis on quick and accurate scanning, the final product provides an intuitive digital solution for storing Magic: The Gathering cards for both collectors and card owners who want to easily store their collection of cards on a computer.

scholarly journals Amharic OCR: An End-to-End Learning

2020 ◽  
Vol 10 (3) ◽  
pp. 1117 ◽  
Author(s):  
Birhanu Belay ◽  
Tewodros Habtegebrial ◽  
Million Meshesha ◽  
Marcus Liwicki ◽  
Gebeyehu Belay ◽  
...  
Keyword(s):  
Recurrent Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Writing System ◽  
Recent Success ◽  
Optical Character ◽  
Proposed Model ◽  
Feature Extractor ◽  
End To End

In this paper, we introduce an end-to-end Amharic text-line image recognition approach based on recurrent neural networks. Amharic is an indigenous Ethiopic script which follows a unique syllabic writing system adopted from an ancient Geez script. This script uses 34 consonant characters with the seven vowel variants of each (called basic characters) and other labialized characters derived by adding diacritical marks and/or removing parts of the basic characters. These associated diacritics on basic characters are relatively smaller in size, visually similar, and challenging to distinguish from the derived characters. Motivated by the recent success of end-to-end learning in pattern recognition, we propose a model which integrates a feature extractor, sequence learner, and transcriber in a unified module and then trained in an end-to-end fashion. The experimental results, on a printed and synthetic benchmark Amharic Optical Character Recognition (OCR) database called ADOCR, demonstrated that the proposed model outperforms state-of-the-art methods by 6.98% and 1.05%, respectively.

scholarly journals Development of Optical Character Recognition Software Package for Mobile Phones

2010 ◽  
pp. 99-104
Author(s):  
Sukhwant Kaur ◽  
H. K. Kaura ◽  
Mritunjay Ojha
Keyword(s):  
Mobile Phones ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
General Purpose ◽  
Desktop Computer ◽  
Text Format ◽  
Software Packages ◽  
Optical Character ◽  
Recognition Software ◽  
Day By Day

Optical Character Recognition (OCR) is a technique through which any textual information contained in images are extracted and converted into editable text format. The various OCR software packages which are available in desktop computer with scanner suffer from one primary constraint- MOBILITY. We have developed an OCR application for mobile phones. All the procedures needed for extracting the text would be performed within the mobile phone, eliminating the need for bulky devices like scanners, desktops and also laptops. Hence it would provide the user the much needed ‘anywhere, anytime’ feature for OCR. The computational power of mobiles is increasing day by day making it easier to run image processing operations for OCR application. Also the resolution of camera in mobile is increasing to match the resolution of scanners. After the document is processed, it can be communicated to another user by email facility of mobile phones as text files. The aim of this paper is to investigate the various issues involved in developing this OCR application in mobile phones. Further design and future scope for this application is elaborated giving insight to the development process. The motivation here was to provide a general purpose framework for OCR application in mobile phones. The framework is developed in a modular fashion.

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.

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