A comparative study of different approaches of primitive printed Arabic Optical Character Recognition

2015 ◽  
Author(s):  
Mohamed Dahi ◽  
Noura A. Semary ◽  
Mohiy M. Hadhoud
Keyword(s):  
Comparative Study ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Optical Character

Performance Analysis of Open Source Optical Character Recognition

2020 ◽  
Vol 17 (9) ◽  
pp. 4267-4275
Author(s):  
Jagadish Kallimani ◽  
Chandrika Prasad ◽  
D. Keerthana ◽  
Manoj J. Shet ◽  
Prasada Hegde ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Comparative Study ◽  
Open Source ◽  
Error Rate ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Accurate Result ◽  
Recognition System ◽  
Optical Character ◽  
Recognition Systems

Optical character recognition is the process of conversion of images of text into machine-encoded text electronically or mechanically. The text on image can be handwritten, typed or printed. Some of the examples of image source can be a picture of a document, a scanned document or a text which is superimposed on an image. Most optical character recognition system does not give a 100% accurate result. This project aims at analyzing the error rate of a few open source optical character recognition systems (Boxoft OCR, ABBY, Tesseract, Free Online OCR etc.) on a set of diverse documents and makes a comparative study of the same. By this, we can study which OCR is the best suited for a document.

Use of Deep Neural Network for Optical Character Recognition

2020 ◽  
pp. 219-254
Author(s):  
Abhishek Das ◽  
Mihir Narayan Mohanty
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Comparative Study ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Deep Neural Network ◽  
Detailed Review ◽  
Image Processing Techniques ◽  
Optical Character ◽  
Processing Techniques

In this chapter, the authors have given a detailed review on optical character recognition. Various methods are used in this field with different accuracy levels. Still there are some difficulties in recognizing handwritten characters because of different writing styles of different individuals even in a particular language. A comparative study is given to understand different types of optical character recognition along with different methods used in each type. Implementation of neural network in different forms is found in most of the works. Different image processing techniques like OCR with CNN, RNN, combination of CNN and RNN, etc. are observed in recent research works.

scholarly journals An Accuracy Examination of OCR Tools

2019 ◽  
Vol 8 (9S4) ◽  
pp. 5-9
Keyword(s):  
Comparative Analysis ◽  
Comparative Study ◽  
Open Source ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Research Paper ◽  
Text Segmentation ◽  
Digital Form ◽  
Optical Character ◽  
Processing Algorithms

In this research paper, the authors have aimed to do a comparative study of optical character recognition using different open source OCR tools. Optical character recognition (OCR) method has been used in extracting the text from images. OCR has various applications which include extracting text from any document or image or involves just for reading and processing the text available in digital form. The accuracy of OCR can be dependent on text segmentation and pre-processing algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, a complex background of image etc. From vehicle number plate the authors tried to extract vehicle number by using various OCR tools like Tesseract, GOCR, Ocrad and Tensor flow. The authors in this research paper have tried to diagnose the best possible method for optical character recognition and have provided with a comparative analysis of their accuracy

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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