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Portable Camera-Based Assistive Text and Product Label Readi

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Dhruvi Patel

on 17 September 2015

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Transcript of Portable Camera-Based Assistive Text and Product Label Readi

Portable Camera-Based Assistive Text and Product Label Reading From Hand-Held Objects for Blind Persons
We propose a camera-based label reader to help blind persons to read names of labels on the products.

Camera acts as main vision in detecting the label image of the product or board then image is processed internally .

And separates label from image , and finally identifies the product and identified product name is pronounced through voice.

Then received label image is converted to text .Once the identified label name is converted to text and converted text is displayed on display unit connected to controller.

Now converted text should be converted to voice to hear label name as voice through ear phones connected to audio.

Walking safely and confidently without any human assistnce in urban or unknown environments is a difficult task for blind people.

Visually impaired people generally use either the typical white cane or the guide dog to travel independently.

But these methods are used only to guide blind people for safe path movement. but these cannot provide any product assistance like shopping.

The system framework consists of three functional components:

Scene capture
Data processing
Audio output
Our main contributions embodied in this prototype system are…..

A novel motion-based algorithm
to solve the aiming problem for blind users by their simply shaking the object of interest for a brief period.

A novel algorithm of automatic text localization
to extract text regions from complex background and multiple text patterns;

A portable camera-based assistive framework to aid blind persons reading text from hand-held objects.

Background subtraction (BGS)
is a conventional and effective approach to detect moving objects for video surveillance systems with stationary cameras.

This method is done based on the frame variations.
Since background imagery is nearly constant in all frames, a Gaussian always compatible

Its subsequent frame pixel distribution is more likely to be the background model

To detect moving objects in a dynamic scene, many adaptive BGS techniques have been developed.

To ensure that the hand-held object appears in the camera view, we employ a camera with a reasonably wide angle in our prototype system (since the blind user may not aim accurately)

This may result in some other extraneous but perhaps text-like objects appearing in the camera view.

To extract the hand-held object of interest from other objects in the camera view,
we ask users to shake the hand-held objects containing the text they wish identify.

then employ a
motion-based method
to localize the objects from cluttered background.

It removes physical hardware requirement .


Accuracy and Flexibility.

Automatic detection

To read printed text on hand-held objects for assisting blind person In order to solve the common aiming problem for blind users.

This method can effectively distinguish the object of interest from background or other objects in the camera view.

To extract text regions from complex backgrounds, we have proposed a novel text localization algorithm based on models of stroke orientation and edge distributions.

OCR is used to perform word recognition on the localized text regions and transform into audio output for blind users.

Our future work will extend our localization algorithm to process text strings with characters fewer than three and to design more robust block patterns for text feature extraction.

We will also extend our algorithm to handle non horizontal text strings.

Furthermore, we will address the significant human interface issues associated with reading text by blind users.

[1]World Health Organization. (2013). 10 facts about blindness and visual impairment [Online]. Available: www.who.int/features/factfiles/blindness/blindness_facts/en/index.html[28]

[2]C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models
for real-time tracking,” presented at the IEEE Comput. Soc.Conf.Comput.
Vision Pattern Recognit., Fort Collins, CO, USA, 2013
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