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Abstract— Braille document
recognition involves image acquisition stage, image preprocessing,
binarization, enhancement, feature extraction and recognition of the
braille script from the given image. The paper concentrates on various methods
for the conversion from Southern Indian Braille script to corresponding
language. The proposed methods were based on histogram technique, Piece wise
enhancement techniques, Image filtering, horizontal profiling, vertical
profiling, image Thresholding, de-skewing the image, template matching etc.
This paper discusses research efforts proposed earlier for recognition the
Southern Indian braille script from a Braille document.I.                
IntroductionResearch
on Braille document recognition plays one of the hot research areas. The Braille encoding system
represent textual documents in a readable format for the visually challenged
persons. As there is a shortage of Braille compatible reading materials,
visually challenged people face trouble in necessities like education and employment.
Also, there are many important Braille Script documents
that are required to be printed so that they can be preserved and retrieved.
There is a need of a system for automatic recognition of Braille documents to
reduce communication
gap between the written text
systems used by sighted persons and access mechanisms through which visually
impaired people can communicate.II.              
Braille SystemBraille
1,2 is a tactile writing system used by people who are visually impaired. Braille
is a system of raised dots that can be read with the fingers by people who are
blind or who have low vision. It is named after Louis Braille, the French man
who invented it. Braille symbols are formed within units of space known as
braille cells. A full braille cell consists of six raised dots arranged in two
parallel rows each having three dots. The dot positions are identified by
numbers from one through six. Sixty-four combinations are possible using one or
more of these six dots. A single cell can be used to represent an alphabet
letter, number, punctuation mark, or even a whole word.It
is possible to transcribe braille by replacing each letter with the braille
code for the letter. This is usually known as Grade 1 Braille. Grade 1 braille
is mostly used by beginners. The basic problem of Grade 1 braille is that
braille letters are much larger than printed ones. Grade 2 braille uses
contractions, which allows to save space and increase reading speed.
Transcribing a text into Grade 2 braille is difficult, and the people doing the
transcription need to have a special education. Grade 3 Braille is a system
that includes many additional contractions. It is almost like a shorthand. It
is rarely used for books, but people use it to be able to write and read fast,
for themselves. It can be used for taking notes. Only very few people can use
grade 4 braille. It uses many rules to shorten grade 3 even further. It allows
a blind person to use shorthand to follow spoken conversation. Very often,
systems of seven or eight dots are used.A.    
Standards
for Braille Embossed on Paper

Every
major braille-producing country has standards for the size and spacing of braille
3. The nominal height of braille dots around 0.48 mm and shall be uniform
within any given transcription. The nominal base diameter of braille dots around
1.44 mm embossed on paper. Cell spacing of dots shall conform to the following:
i) the nominal distance from center to center of adjacent dots (horizontally or
vertically, but not diagonally) in the same cell shall be 2.340 mm ii) the
nominal distance from center to center of corresponding dots in adjacent cells
shall be 6.2 mm. The nominal line spacing of braille cells from center to
center of nearest corresponding dots in adjacent lines shall be 1.0 cm. Braille
cell dimensions (in inches) is shown in the figure 1.A.    
Southern
Indian Language Braille SystemThe Dravidian
languages are a language  spoken mainly in southern
India and parts of eastern and central India, as well as
in Sri
Lanka , southern Afghanistan, Nepal, Bangladesh and Bhutan, and
overseas in other countries such as Malaysia, Indonesia and  Singapore.
The Dravidian languages with the most speakers are Telugu, Tamil, Kannada and Malayalam. 

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Braille
is used by thousands of people all over the world in their native languages,
and provides a means of literacy for all. Bharati braille 4  or  Bhartiya Braille (Indian
braille), is a largely unified braille script
for writing the languages of India. When India gained
independence, eleven braille scripts were in use, in different parts of the
country and for different languages. By 1951 a single national standard had
been settled on, Bharati braille, which has since been adopted by Sri
Lanka, Nepal, and Bangladesh.
Bharati
Braille is the adaptation of the six-dot system for the languages of India. The
Tamil, Telugu, Malayalam and Kannada braille alphabets sheets are shown in
Figure 2, Figure 3, Figure 4 and Figure 5 respectively.I.                
Southern Indian Language
Braille System Recognition TechniquesBraille 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 et.al 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.Padmavati et.al 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 et.al 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 et.al 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.Bijet et.al
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 et.al 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.II.              
Conclusion & Future
work

Braille
has been developed as the reading and writing system for the visually
challenged people. Few methods have been proposed in the past for recognition
of Braille script in Southern Indian Languages based on histogram technique,
piece wise enhancement techniques, image filtering, horizontal profiling,
vertical profiling, image thresholding, de-skewing the image, template matching
etc. These approaches considered the different attributes related to
orientation, alignment, contrast, color, intensity, connected-components, edges
etc. These attributes are used to classify dot regions from their background or
other regions within the image. This paper provides a study of the Braille recognition in Southern Indian
Languages proposed earlier. Every method has its own benefits and limitations. The
future work mainly focusses on developing a single unified algorithm for efficient
and better braille document recognition in Southern Indian Languages.

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