High accuracy optical character recognition algorithms using learning array of ANN

2014 ◽  
Author(s):  
B. Vani ◽  
M. Shyni Beaulah ◽  
R. Deepalakshmi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
High Accuracy ◽  
Optical Character ◽  
Recognition Algorithms

scholarly journals iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing

2021 ◽  
Author(s):  
Menbere Kina Tekleyohannes ◽  
Vladimir Rybalkin ◽  
Muhammad Mohsin Ghaffar ◽  
Javier Alejandro Varela ◽  
Norbert Wehn ◽  
...  
Keyword(s):  
Power Consumption ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
High Accuracy ◽  
Document Image ◽  
System On Chip ◽  
Historical Documents ◽  
Optical Character ◽  
Historical Archives ◽  
On Chip

AbstractIn recent years, $$\hbox {optical character recognition (OCR)}$$ optical character recognition (OCR) systems have been used to digitally preserve historical archives. To transcribe historical archives into a machine-readable form, first, the documents are scanned, then an $$\hbox {OCR}$$ OCR is applied. In order to digitize documents without the need to remove them from where they are archived, it is valuable to have a portable device that combines scanning and $$\hbox {OCR}$$ OCR capabilities. Nowadays, there exist many commercial and open-source document digitization techniques, which are optimized for contemporary documents. However, they fail to give sufficient text recognition accuracy for transcribing historical documents due to the severe quality degradation of such documents. On the contrary, the anyOCR system, which is designed to mainly digitize historical documents, provides high accuracy. However, this comes at a cost of high computational complexity resulting in long runtime and high power consumption. To tackle these challenges, we propose a low power energy-efficient accelerator with real-time capabilities called iDocChip, which is a configurable hybrid hardware-software programmable $$\hbox {System-on-Chip (SoC)}$$ System-on-Chip (SoC) based on anyOCR for digitizing historical documents. In this paper, we focus on one of the most crucial processing steps in the anyOCR system: Text and Image Segmentation, which makes use of a multi-resolution morphology-based algorithm. Moreover, an optimized $$\hbox {FPGA}$$ FPGA -based hybrid architecture of this anyOCR step along with its optimized software implementations are presented. We demonstrate our results on multiple embedded and general-purpose platforms with respect to runtime and power consumption. The resulting hardware accelerator outperforms the existing anyOCR by 6.2$$\times$$ × , while achieving 207$$\times$$ × higher energy-efficiency and maintaining its high accuracy.

scholarly journals Bubble Captcha - A Start of the New Direction of Text Captcha Scheme Development

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Ondrej Bostik ◽  
Karel Horak ◽  
Jan Klecka
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Turing Test ◽  
Simple Approach ◽  
Internet Services ◽  
Malicious Activity ◽  
Optical Character ◽  
Recognition Algorithms ◽  
The World ◽  
Human Capability

CAPTCHA, A Completely Automated Public Turing test to tell Computers and Humans Apart, iswell-known system widely used in all sorts of internet services around the world designated to secure the webfrom an automatic malicious activity. For almost two decades almost every system utilize a simple approach tothis problem containing a transcription of distorted letters from image to a text eld. The ground idea is to useimperfection of Optical Character Recognition algorithms against the computers. The development of OpticalCharacter recognition algorithms leads only to state, where the CAPTCHA schemes become more complex andhuman users have a great di culty with the transcription.This paper aims to present a new way of development of CAPTCHA schemes based more a human perception.The goal of this work is to implement new Captcha scheme and assess human capability to read unusual fontsnewer seen before.

scholarly journals Multi-modal video retrieval using Dilated Pyramidal Residual network

2019 ◽  
Vol 2 (5) ◽  
pp. 138-143
Author(s):  
An Ngoc Thuy La ◽  
Dat Phuoc Nguyen ◽  
Nhut Minh Pham ◽  
Quan Hai Vu
Keyword(s):  
Automatic Speech Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Video Retrieval ◽  
High Accuracy ◽  
Research Topic ◽  
Residual Network ◽  
Sequence Recognition ◽  
Optical Character ◽  
Classification Tasks

Pyramidal Residual Network achieved high accuracy in image classification tasks. However, there is no previous work on sequence recognition tasks using this model. We presented how to extend its architecture to form Dilated Pyramidal Residual Network (DPRN), for this long-standing research topic and evaluate it on the problems of automatic speech recognition and optical character recognition. Together, they formed a multi-modal video retrieval framework for Vietnamese Broadcast News. Experiments were conducted on caption images and speech frames extracted from VTV broadcast videos. Results showed that DPRN was not only end-to-end trainable but also performed well in sequence recognition tasks.

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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