Notice of Violation of IEEE Publication Principles: A new approach for rotation invariant optical character recognition using eigendigit

2007 ◽  
Author(s):  
Maruf Monwar ◽  
Waqar Haque ◽  
Padma Polash Paul
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Rotation Invariant ◽  
New Approach ◽  
Optical Character

scholarly journals Recognition of Handwritten Characters using Deep Convolutional Neural Network

2019 ◽  
Vol 8 (6S4) ◽  
pp. 314-317
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Writing Style ◽  
Handwritten Character Recognition ◽  
New Approach ◽  
Optical Character ◽  
Handwritten Character ◽  
Handwritten Recognition ◽  
Difficult Part

Handwritten character recognition (HCR) mainly entails optical character recognition. However, HCR involves in formatting and segmentation of the input. HCR is still an active area of research due to the fact that numerous verification in writing style, shape, size to individuals. The main difficult part of Indian handwritten recognition has overlapping between characters. These overlapping shaped characters are difficult to recognize that may lead to low recognition rate. These factors also increase the complexity of handwritten character recognition. This paper proposes a new approach to identify handwritten characters for Telugu language using Deep Learning (DL). The proposed work can be enhance the recognition rate of individual characters. The proposed approach recognizes with overall accuracy is 94%.

scholarly journals Demystification of Bilingual Optical Character Recognition System for Devanagari and English Scripts

2019 ◽  
Vol 9 (1) ◽  
pp. 5180-5185
Keyword(s):  
Pattern Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Research Paper ◽  
New Approach ◽  
Optical Character ◽  
New Ideas

Research is deliberately going on in the field of pattern recognition. New ideas are developed and implemented in this field throughout the globe. Optical Character Recognition (OCR) is one of the inseparable applications of Pattern Recognition. Though extensive research is already reported in this field, but multilingual Optical Character Recognition is the most challenging aspect which is still, the need of the hour. Myriads of researchers are digging the information to gather the best solutions for the recognition purpose. In this research paper, we are purposing the steps for the recognition of Devanagari and English scripts simultaneously occurring in the documents. A new approach of segmentation and splitting the characters of both the scripts is also introduced for the benefits of researchers. Most commonly in the documents containing English and Devanagari scripts, English characters are already separated, the challenge is to separate the Devanagari characters. Algorithm to implement the challenging aspect to segment the Devanagari and Roman scripts simultaneously is also implemented in the present paper.

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 Printed Persian Subword Recognition Using Wavelet Packet Descriptors

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Samira Nasrollahi ◽  
Afshin Ebrahimi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Text Documents ◽  
New Approach ◽  
Feature Vectors ◽  
Invariant Features ◽  
Optical Character ◽  
The Mean

In this paper, we present a new approach to offline OCR (optical character recognition) for printed Persian subwords using wavelet packet transform. The proposed algorithm is used to extract font invariant and size invariant features from 87804 subwords of 4 fonts and 3 sizes. The feature vectors are compressed using PCA. The obtained feature vectors yield a pictorial dictionary for which an entry is the mean of each group that consists of the same subword with 4 fonts in 3 sizes. The sets of these features are congregated by combining them with the dot features for the recognition of printed Persian subwords. To evaluate the feature extraction results, this algorithm was tested on a set of 2000 subwords in printed Persian text documents. An encouraging recognition rate of 97.9% is got at subword level recognition.

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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