scholarly journals A Survey On Telugu Optical Character Recognition From Digital Images

2021 ◽  
Vol 12 (6) ◽  
pp. 999-1003
Author(s):  
Srinivasa Rao Dhanikonda, Et. al.
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Digital Images ◽  
Electronic Media ◽  
Vital Role ◽  
Indian Languages ◽  
Form Processing ◽  
Optical Character ◽  
Essential Function ◽  
Share Information

Images play an essential function in the electronic media to share information. Nowadays, each event is going to be recorded in the arrangement of digital images. Text from the image file won't be in a format on the computer. OCR (Optical Character Recognition) for English vocabulary is well constructed. Currently, there's a requirement of OCR for Indian languages to maintain historical documents composed mainly in Indian languages to arrange publications in the library and for program form processing. OCR for the Telugu language is challenging as consonants and vowels plays a vital role in forming words along with vattus and gunithas. It may be a mixture of vowels and consonants that may form a compound character. This paper presents research on methods utilized in the OCR method for the Telugu Language until today.

A Zone Based Approach for Classification and Recognition Of Telugu Handwritten Characters

2016 ◽  
Vol 6 (4) ◽  
pp. 1647
Author(s):  
N. Shobha Rani ◽  
Sanjay Kumar Verma ◽  
Anitta Joseph
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Vital Role ◽  
Indian Languages ◽  
Processing Application ◽  
Image Objects ◽  
South Indian ◽  
Recognition Systems ◽  
The Times

Realization of high accuracies and efficiencies in South Indian character recognition systems is one of the principle goals to be attempted time after time so as to promote the usage of optical character recognition (OCR) for South Indian languages like Telugu. The process of character recognition comprises pre-processing, segmentation, feature extraction, classification and recognition. The feature extraction stage is meant for uniquely recognizing each character image for the purpose of classifying it. The selection of a feature extraction algorithm is very critical and important for any image processing application and mostly of the times it is directly proportional to the type of the image objects that we have to identify. For optical technologies like South Indian OCR, the feature extraction technique plays a very vital role in accuracy of recognition due to the huge character sets. In this work we mainly focus on evaluating the performance of various feature extraction techniques with respect to Telugu character recognition systems and analyze its efficiencies and accuracies in recognition of Telugu character set.

KANNADA ALPHABETS RECOGNITION WITH APPLICATION TO BRAILLE TRANSLATION

2011 ◽  
Vol 11 (03) ◽  
pp. 293-314
Author(s):  
SIDDHALING UROLAGIN ◽  
K. V. PREMA ◽  
N. V. SUBBA REDDY
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Prior Knowledge ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Research Work ◽  
Indian Languages ◽  
Indian Language ◽  
Optical Character ◽  
Natural Way

In this paper, an effort is made to apply optical character recognition (OCR) for Braille translation on Kannada characters. In general, OCR systems for Indian language are more complex due to larger number of vowels, consonants, and conjuncts and Indian languages are inflectional and agglutinative in nature. Specifically, characters of Kannada script have higher similarity in shape and higher variability across fonts, making recognition of characters a difficult task. A decision tree is developed in this research work. The main motivations are that decision trees provide a natural way to incorporate prior knowledge of domain and many Kannada characters have similar looking shapes. The similar looking characters can be grouped and then further partitioned into categories at various levels to effectively create a decision tree. To facilitate this, three modular classifiers are developed based on the nature of Kannada characters. These modular classifiers are employed at nodes of the decision tree. The Braille equivalent of Kannada characters is obtained by translation rules. An overall accuracy of classification and Braille translation of 93.80% is obtained.

scholarly journals A Zone Based Approach for Classification and Recognition Of Telugu Handwritten Characters

2016 ◽  
Vol 6 (4) ◽  
pp. 1647
Author(s):  
N. Shobha Rani ◽  
Sanjay Kumar Verma ◽  
Anitta Joseph
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Vital Role ◽  
Indian Languages ◽  
Processing Application ◽  
Image Objects ◽  
South Indian ◽  
Recognition Systems ◽  
The Times

Realization of high accuracies and efficiencies in South Indian character recognition systems is one of the principle goals to be attempted time after time so as to promote the usage of optical character recognition (OCR) for South Indian languages like Telugu. The process of character recognition comprises pre-processing, segmentation, feature extraction, classification and recognition. The feature extraction stage is meant for uniquely recognizing each character image for the purpose of classifying it. The selection of a feature extraction algorithm is very critical and important for any image processing application and mostly of the times it is directly proportional to the type of the image objects that we have to identify. For optical technologies like South Indian OCR, the feature extraction technique plays a very vital role in accuracy of recognition due to the huge character sets. In this work we mainly focus on evaluating the performance of various feature extraction techniques with respect to Telugu character recognition systems and analyze its efficiencies and accuracies in recognition of Telugu character set.

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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