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Braille document
recognition involves image capturing stage, preprocessing, binarization, enhancement,
feature extraction and recognition of the braille script from the given braille
image. Earlier works on Braille character recognition shows that there are very
few research works on recognition of southern Indian language Braille
characters. Recognition of English Braille script to corresponding English apathetes is easy compared to Southern
Indian languages as there are more than 260 characters in Dravidian languages. The
64 combinations available by the 6 dots of a Braille cell can be used easily for representing
all characters of English.

Santhoosh 5 presented
a research to Tamil braille character recognition based on camera assistive device, an embedded
system bulit on Raspberry Pi board. As a first step the captured
image was converted to gray image and the image was cropped according to the
requirement. Adaptive
thresholding technique was used to separate the Braille dots from the background.
Morphology techniques were used to enhance the image and binary search
algorithm was used to correct if any de-skewing in the image. Dot parts were
detected from the image and equivalent braille character was recognized using
matching algorithm. The methodology used in this paper was experimented on
Thirukkural Braille Book and achieved a result of more than 90% accuracy.

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Padmavati 6 projected a research to convert
Braille script document into its corresponding letters
of 3 languages i.e English, Tamil and Hindi. Pre- processing techniques
like Gaussian filter were performed on the Braille document
to improve the dots and to eliminate the noise. Piece wise enhancement
techniques such as contrast stretching, intensity stretching was used for
enhancing the dots. As a next step, the edge detectors and projection
profile method were applied to crop the interest area. The
image was first separated into lines and then into Braille cells by
applying horizontal and vertical projection profiles. A Binary pattern vector
of length 6 for each Braille cell was generated. Binary 1 and 0 were used to
represent presence and absence of braille dot respectively. The corresponding alphabet were generated using its pre-built
match table.

Srinath 7
presented an Optical Braille character recognition system for Kannada Braille document.
The project took the image of Kannada Braille script and segmented the image in
to line by using the relative position of the dots.  After line
segmentation was done, Braille characters were separated using inter character
distance parameter. The recognized Braille character was translated into Kannada
alphabet and saved in a document. The methodology used in this paper
achieved more than 98% accuracy.

Ravi 8 projected
a research to convert hand punched Kannada Braille Characters using knowledge
based multi decision method. Braille dots were carefully separated depending on the location of the dots. The inter character
distance was used to group the dots into a word box. The system was designed to
recognize the braille dot and it was converted to Kannada character.

9 presented a research work to convert Odia, Hindi, Telugu
and English braille documents into its corresponding language.
The algorithm used the
technique of histogram analysis, segmentation, pattern recognition, letter
arrays, data base generation with testing in software and dumping in using
Spartan 3e FPGA kit which defined the dot patterns for the alphabets.

Sudhir Rao et al. 10 proposed a research work to convert Kannada Braille document taken by a
camera, into Kannada script or audio. As a first step the color image of input
image was converted to unicolor
space for processing. An automated thresholding algorithm was applied to
get the area of interest and segmentation technique was applied and recognition
of letter was done based
on highlighted dot in Braille document.  All algorithms were implemented for a Xilinx
Spartan 3E FPGA using Verilog HDL language and
were executed in real time. An accuracy of over 94% was attained in Braille
segmentation and detection.

Ann Jose 11 projected a
work on changing the Malayalam Braille document to text and concatenative
speech synthesis
technique was used for speech conversion. The image was captured by CMOS image
sensor and then the image was converted to binary image by calculating the
threshold using histogram analysis. Filtering technique was applied to remove
the noise. The row and column grouping of dots was done based on the spacing
between dots to identify the Braille cell. Presence of dot will be represented
as 1 and absence as 0 and a six-digit binary number was generated. Using this
binary number Malayalam character mapping was done and speech synthesizer was
used for audio conversion.

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