scholarly journals Efficient Morphological Analysis Using Arbitrary Structuring Elements for Security Purposes

2013 ◽  
pp. 80-85
Author(s):  
Akansha Saxena ◽  
Santosh Kumar
Keyword(s):  
Mathematical Morphology ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Morphological Analysis ◽  
Image Data ◽  
Speckle Noise ◽  
Boundary Extraction ◽  
Structuring Element ◽  
Optical Character ◽  
Salt And Pepper

The term Mathematical Morphology (MM) mostly deals with the mathematical theory of describing shapes using sets. In morphology, images are represented as sets. This task is investigated by the interaction between an image and a certain chosen arbitrary structuring element using the basic operations of erosion and dilation. The various applications of morphologyinclude skeletonization, prunning, optical character recognition,image analysis,artifacts removal,boundary extraction, etc. It is further extended by the fact that mathematical morphology provides better quality image data for analysis and diagnostic purposes. The process is very efficient due to the use of MATLAB algorithmswhich are helpful for securing meaningful information against different threats like-speckle noise, salt and pepper noise,etc.

scholarly journals Robust Combined Binarization Method of Non-Uniformly Illuminated Document Images for Alphanumerical Character Recognition

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2914
Author(s):  
Hubert Michalak ◽  
Krzysztof Okarma
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Accuracy ◽  
Image Data ◽  
Document Images ◽  
Historical Document ◽  
Image Binarization ◽  
Optical Character ◽  
Binarization Method ◽  
Camera Sensors

Image binarization is one of the key operations decreasing the amount of information used in further analysis of image data, significantly influencing the final results. Although in some applications, where well illuminated images may be easily captured, ensuring a high contrast, even a simple global thresholding may be sufficient, there are some more challenging solutions, e.g., based on the analysis of natural images or assuming the presence of some quality degradations, such as in historical document images. Considering the variety of image binarization methods, as well as their different applications and types of images, one cannot expect a single universal thresholding method that would be the best solution for all images. Nevertheless, since one of the most common operations preceded by the binarization is the Optical Character Recognition (OCR), which may also be applied for non-uniformly illuminated images captured by camera sensors mounted in mobile phones, the development of even better binarization methods in view of the maximization of the OCR accuracy is still expected. Therefore, in this paper, the idea of the use of robust combined measures is presented, making it possible to bring together the advantages of various methods, including some recently proposed approaches based on entropy filtering and a multi-layered stack of regions. The experimental results, obtained for a dataset of 176 non-uniformly illuminated document images, referred to as the WEZUT OCR Dataset, confirm the validity and usefulness of the proposed approach, leading to a significant increase of the recognition accuracy.

Optical character recognition and the smart ancient script database

2020 ◽  
Vol 4 (4) ◽  
pp. 255-269
Author(s):  
Zhiji Liu
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Interdisciplinary Collaboration ◽  
Image Data ◽  
The Other ◽  
Linguistic Context ◽  
Mutual Support ◽  
Optical Character ◽  
Push Forward ◽  
Multi Level

The initial success of optical character recognition (OCR) for ancient scripts has opened the floodgates for ‘smart’ ancient script research. ‘Smart’ ancient script research requires the support of a smart ancient script database. 
In order to compile the big data necessary for this smart research, smart ancient script database software must be able to recognize all aspects and all levels of all ancient script materials. Therefore, in addition to the integration of OCR functionality into this software, the other primary imperative moving forward is to innovate a new digitized ancient script data system, one that includes full-scale supplementation to include all available materials, as well as newly inputted image data. This data must include variant graphic forms, variant written forms, handwriting, graphic components, calligraphic styles, and other of the inexhaustible different variations in script construction. This database must contain a multi-level framework with an annotated arrangement of the fullest range of meanings for words within linguistic context. It must also contain a digitally integrated multiple-path indexed arrangement of the important paleographical interpretations in the field. Our strategy for the construction of this smart ancient script database is to push forward with both algorithm writing and data input work simultaneously and in mutual support, following an open-sourced community supported model, making this project an exercise in interdisciplinary collaboration within the paleography community.

scholarly journals Implementation of Optical Character Recognition Using Raspberry Pi for Visually Challenged Person

2018 ◽  
Vol 7 (3.34) ◽  
pp. 65 ◽  
Author(s):  
S Thiyagarajan ◽  
Dr G.Saravana Kumar ◽  
E Praveen Kumar ◽  
G Sakana
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Speech Synthesis ◽  
Native Speaker ◽  
Image Data ◽  
Raspberry Pi ◽  
Spoken Discourse ◽  
Optical Character ◽  
Digital Format ◽  
Visually Challenged

Blind people are unable to perform visual tasks. The majority of published printed works does not include Braille or audio versions, and digital versions are still a minority. In this project, the technology of optical character recognition (OCR) enables the recognition of texts from image data. The system is constituted by the raspberry pi, HD camera and Bluetooth headset. This technology has been widely used in scanned or photographed documents, converting them into electronic copies. The technology of speech synthesis (TTS) enables a text in digital format to be synthesized into human voice and played through an audio system. The objective of the TTS is the automatic conversion of sentences, without restrictions, into spoken discourse in a natural language, resembling the spoken form of the same text, by a native speaker of the language.  

Application of Mathematical Morphology on Touching or Broken Characters Processing

2010 ◽  
Vol 171-172 ◽  
pp. 73-77
Author(s):  
Ying Jie Liu ◽  
Fu Cheng You
Keyword(s):  
Image Processing ◽  
Mathematical Morphology ◽  
Digital Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Binary Image ◽  
Practical Applications ◽  
Optical Character ◽  
Individual Character

It is difficult to process touching or broken characters in practical applications on optical character recognition. For touching or broken characters, a method based on mathematical morphology of binary image is put forward in the paper. On the basis of the relative theories of digital image processing, the overall process is introduced including separation of touching characters and connection of broken characters. First of all, character image is pre-processed through smoothing and threshold segmentation in order to generate binary image of characters. Then character regions which are touching or broken are processed through different operators of mathematical morphology of binary image by different structuring elements. Thus the touching characters are separated and broken characters are connected. For higher recognition rate, further processes are done to achieve normal and individual character regions.

scholarly journals A modified K3M thinning algorithm

2016 ◽  
Vol 26 (2) ◽  
pp. 439-450 ◽  
Author(s):  
Marek Tabedzki ◽  
Khalid Saeed ◽  
Adam Szczepański
Keyword(s):  
Data Reduction ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Image Data ◽  
Robust Solution ◽  
Thinning Algorithm ◽  
Optical Character ◽  
Weak Points ◽  
Proposed Modification ◽  
General Method

Abstract The K3M thinning algorithm is a general method for image data reduction by skeletonization. It had proved its feasibility in most cases as a reliable and robust solution in typical applications of thinning, particularly in preprocessing for optical character recognition. However, the algorithm had still some weak points. Since then K3M has been revised, addressing the best known drawbacks. This paper presents a modified version of the algorithm. A comparison is made with the original one and two other thinning approaches. The proposed modification, among other things, solves the main drawback of K3M, namely, the results of thinning an image after rotation with various angles.

scholarly journals An efficient extraction of information from Indian Government issued documents Aadhar and Pan Card

2021 ◽  
pp. 56-61
Author(s):  
Rachna Tewani ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Data Collection ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Image Data ◽  
Indian Government ◽  
Use Of Data ◽  
Optical Character ◽  
Efficient Extraction

In today's world, everything is getting digitized, and widespread use of data scanning tools and photography. When we have a lot of image data, it becomes important to accumulate data in a form that is useful for the company/organization. Doing it manually is a tedious task and takes an ample amount of time. Hence to simplify the job, we have developed a FLASK API that takes an image folder as an object and returns an excel sheet of relevant data from the image data. We have used optical character recognition and software like pytesseract to extract data from images. Further in the process, we have used natural language processing, and finally, we have found relevant data using the globe and regex module. This model is helpful in data collection from Registration certificates which helps us store data like chassis number, owner name, car number, etc., easily and can be applied to Aadhaar cards and pan cards.

scholarly journals Business Process Automation: A Workflow Incorporating Optical Character Recognition and Approximate String and Pattern Matching for Solving Practical Industry Problems

2019 ◽  
Vol 2 (4) ◽  
pp. 33 ◽  
Author(s):  
Coenrad de Jager ◽  
Marinda Nel
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Business Processes ◽  
String Matching ◽  
Image Data ◽  
Second Step ◽  
Approximate String Matching ◽  
Process Automation ◽  
Optical Character ◽  
Image Digitization

Companies are relying more on artificial intelligence and machine learning in order to enhance and automate existing business processes. While the power of OCR (Optical Character Recognition) technologies can be harnessed for the digitization of image data, the digitalized text still needs to be validated and enhanced to ensure that data quality standards are met for the data to be usable. This research paper focuses on finding and creating an automated workflow that can follow image digitization and produce a dictionary consisting of the desired information. The workflow introduced consists of a three-step process that is implemented after the OCR output has been generated. With the introduction of each step, the accuracy of key-value matches of field names and values is increased. The first step takes the raw OCR output and identifies field names using exact string matching and field-values using regular expressions from an externally maintained file. The second step introduces index pairing that matches field-values to field names based on the location of the field name and value on the document. Finally, approximate string matching is introduced to the workflow, which increases accuracy. By implementing these steps, the F-measure for key-value pair matches is measured at 60.18% in the first step, 80.61% once index pairing is introduced, and finally 90.06% after approximate string matching is introduced. The research proved that accurate usable data can be obtained automatically from images with the implementation of a workflow after OCR.

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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