scholarly journals A Study of Novel Optical Character Recognition Algorithms

2021 ◽  
Vol 23 (06) ◽  
pp. 301-305
Author(s):  
Roshan Suvaris ◽  
◽  
Dr. S Sathyanarayana ◽  
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Human Life ◽  
Handwriting Recognition ◽  
Digital Storage ◽  
Optical Character ◽  
Pros And Cons ◽  
Computer Text ◽  
Almost All ◽  
Textual Content

Optical Character Recognition has been an inseparable part of human life during everyday transactions. The OCR has extended its application areas in almost all fields viz. healthcare, finance, banking, entertainment, trading system, digital storage, and so on. In the recent past, handwriting recognition is one of the hardest study areas in the area of image processing. In this paper, the various techniques for converting textual content from number plates, printed, handwritten paper documents into machine code have been discussed. The transforming method used in all these techniques is known as OCR. The English OCR system is necessary for the conversion of various published books and other documents in English into human editable computer text files. The latest researches in this area have included methodologies that identify different fonts and styles of English handwritten scripts. As of date, even though a number of algorithms are available, it has its own pros and cons. Since the recognition of different styles and fonts in machine-printed and handwritten English script is the biggest challenge, this field is open for researchers to implement new algorithms that would overcome the deficiencies of its predecessors.

scholarly journals Model for Converting PDF to Audio Format (Listen Your Book)

2021 ◽  
Vol 9 (VII) ◽  
pp. 3203-3206
Author(s):  
Shailendra Singh
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Efficient Technique ◽  
Text To Speech ◽  
Multiple Methods ◽  
Optical Character ◽  
Visually Impaired People ◽  
The People ◽  
Textual Content ◽  
Text Images

The present paper has introduced an innovative and efficient technique that enables user to hear the contents of text images instead of reading through them. In the current world, there is a great increase in the utilization of digital technology and multiple methods are available for the people to capture images. such images may contain important textual content that the user may need to edit or store digitally. It merges the concept of Optical Character Recognition (OCR) and Text to Speech Synthesizer (TTS). This can be done using Optical Character Recognition with the use of Tesseract OCR Engine. OCR is a branch of AI that is used in applications to recognize text from scanned documents or images. The analyzed text can also be converted to audio format to help visually impaired people hear the content that they wish to know. Text-to-Speech conversion is a method that scans and reads alphabets and numbers that are in the image using OCR technique and convert it into voices. The aim is to study and compare the multiple methods used for STT conversions and to figure out the most efficient technique that can be adapted for the conversion processes. As a result, based on review study it is found that HMM is a statistical model which is most suitable for TTS conversions.

Analysis of Text Identification Techniques Using Scene Text and Optical Character Recognition

2021 ◽  
Vol 11 (4) ◽  
pp. 39-62
Author(s):  
Monica Gupta ◽  
Alka Choudhary ◽  
Jyotsna Parmar
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Short Term Memory ◽  
Handwriting Recognition ◽  
Text Recognition ◽  
Stroke Width ◽  
Optical Character ◽  
Scene Text ◽  
Benchmark Datasets ◽  
Scene Text Recognition

In today's era, data in digitalized form is needed for faster processing and performing of all tasks. The best way to digitalize the documents is by extracting the text from them. This work of text extraction can be performed by various text identification tasks such as scene text recognition, optical character recognition, handwriting recognition, and much more. This paper presents, reviews, and analyses recent research expansion in the area of optical character recognition and scene text recognition based on various existing models such as convolutional neural network, long short-term memory, cognitive reading for image processing, maximally stable extreme regions, stroke width transformation, and achieved remarkable results up to 90.34% of F-score with benchmark datasets such as ICDAR 2013, ICDAR 2019, IIIT5k. The researchers have done outstanding work in the text recognition field. Yet, improvement in text detection in low-quality image performance is required, as text identification should not be limited to the input quality of the image.

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.

Pengantar dan Survey Tentang Optical Music Recognition

2014 ◽  
Vol 6 (1) ◽  
pp. 36-39
Author(s):  
Kevin Purwito
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Optical Music Recognition ◽  
Optical Character ◽  
Digital Format ◽  
Music Recognition ◽  
Index Terms ◽  
The Many ◽  
Further Development ◽  
Music Symbol

This paper describes about one of the many extension of Optical Character Recognition (OCR), that is Optical Music Recognition (OMR). OMR is used to recognize musical sheets into digital format, such as MIDI or MusicXML. There are many musical symbols that usually used in musical sheets and therefore needs to be recognized by OMR, such as staff; treble, bass, alto and tenor clef; sharp, flat and natural; beams, staccato, staccatissimo, dynamic, tenuto, marcato, stopped note, harmonic and fermata; notes; rests; ties and slurs; and also mordent and turn. OMR usually has four main processes, namely Preprocessing, Music Symbol Recognition, Musical Notation Reconstruction and Final Representation Construction. Each of those four main processes uses different methods and algorithms and each of those processes still needs further development and research. There are already many application that uses OMR to date, but none gives the perfect result. Therefore, besides the development and research for each OMR process, there is also a need to a development and research for combined recognizer, that combines the results from different OMR application to increase the final result’s accuracy. Index Terms—Music, optical character recognition, optical music recognition, musical symbol, image processing, combined recognizer  

Sign in / Sign up

Export Citation Format

Share Document