Text Extraction from Images by Neural Network using MATLAB - Matlab Simulink for E & T

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Saturday, August 1, 2020

Text Extraction from Images by Neural Network using MATLAB

Text Extraction from Images by Neural Network using MATLAB

Text Extraction from Images by Neural Network using MATLAB Software

The volume of multimedia database has increased exponentially due to the technology advancement in the area of computer processor and storage devices. 

Unfortunately these large multimedia repositories are not indexed and are accessible only by sequential scanning of entire multimedia archive. 

To navigate or browse a large multimedia database is cumbersome and time consuming. The popular web based search engines like Google, Yahoo and AltaVista provide users with a content-based search model in order to access the World Wide Web pages and multimedia. 

But in this typical text based search engine, images and videos are manually annotated by identifying limited number of keywords that describe their visual information and content. However, for image and video retrieval, it is not an effective solution.


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  • Ø Extraction of text from image is an important step.
  • Ø Initially the text image is given as input.

  • Ø This input image is subjected to banalization , feature extraction and finally text extraction by neural network (NN)




As discussed earlier, in text based search engine, images and videos are manually annotated by identifying limited number of keywords that describe their visual information and content. Some images may be related differently by different people. Secondly, it is not always possible to identify all desired keywords by manual text descriptors. 

Thirdly, sequential examination of entire video content for large growing multimedia archives is required for identifying keywords. 

This manual indexing process of image content by document lists will be increasingly tedious and time consuming. This way of manual indexing is not cost effective and the efficiency of indexing becomes highly dependent on quality of manpower and finally, it is language dependent.

Text has compact, distinctive visual characteristics i.e. a set of symbols with distinct geometrical and morphological features.

Secondly text may be of different font, color or language is usually closely related to its semantic content and maintaining some specific pattern in the image. 

Hence, text is often considered to be a strong candidate for use as a feature in high level semantic indexing and content-based retrieval. 

Text is useful in performing text analysis like in broadcasting, to display name of the program, anchor’s name, program introductions, special announcements. 

In an advertisement product’s name, name of the companies selling the products are displayed. In weather forecast, temperature, humidity of different places is shown. 

In other cases objects and locations can be identified by text from implicit and explicit text annotations such as in a sports event players can be identify by their name and number in their jerseys, vehicles can be spotted by their license plate, a station or streets or shops can be located by their bill boards or hoardings.


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Results: 

Input image

Obtained output text


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