This document discusses optical character recognition (OCR) for Bangla handwritten text. It notes that while OCR is widely used to convert text to electronic formats, OCR for handwritten Bangla is still rare due to the challenge of infinite variations in individual handwriting styles. The authors aim to create an OCR with considerable accuracy for handwritten Bangla text. They take a bottom-up approach, applying their methodology to specific samples and improving it based on performance until reaching a general solution. Their current focus is the segmentation step, and they have tested thinning/run-length reduction and projection methods, but faced issues with both.

1. OPTICAL CHARACTER
RECOGNITION FOR
BANGLA HANDWRITTEN
TEXT

2. INTRODUCTION
 Optical character recognition,(OCR), is
the mechanical or electronic translation of
scanned images of handwritten, typewritten or
printed text into machine-encoded text.
 It is widely used to convert books and documents
into electronic files, to computerize a record-keeping
system in an office, or to publish the text on a
website.
 OCR makes it possible to edit the text, search for a
word or phrase, store it more compactly.

3. Today we have OCRs easily available for the
English language . We can find OCRs for printed
Bengali as well but OCRs for handwritten Bengali are
very rare. And those which are available do not have
a decent recognition accuracy.
We aim to create such an OCR which
gives us a considerable recognition accuracy for
handwritten Bengali.

4. PROBLEM
Now we are creating an OCR for handwritten
Bengali text. The main problem arises due to
the fact that we are doing it for handwritten
text. So our sample set is very infinite. Also
different samples have different
characteristics. The handwriting samples are
collected from different persons, hence it is
very unlikely that they will follow a similar
pattern.

5. OUR APPROACH
We have followed a bottom up method
in our approach, i.e. we start with a specific
sample , and then approach towards the
general solution. We take a particular sample
, apply our methodology to it and find out the
results. Then we re-perform the computation
on a second sample set and depending upon
the performance of our methodology on this
set we keep on improving our process until it
alludes towards a general solution.

6. Presently we are in our first step of the method :
SEGMENTATION.
And the methods which we have used are :
Thinning and Run length Reduction
Projection Along Column Scan lines

7. Thinning and Run length Reduction
Thinning basically is reducing the density of the
characters ……….

8. But we faced some difficulties in this approach :
This method was becoming too much
dependent on the handwriting which is not
desirable.
The segmentation of the ‘matras’ and the
character resulted in some gaps in the
character itself which was not easy to fill in.
The segmentation obtained was not optimum.

9. PROJECTION
In this method we project the intensity of
each and every character (here the ‘matras’ of
the character are also taken into account
along with the character). And for every
straight line we get a peak value through
which we can identify the presence of the
‘matra’.

10. This method too has its own disadvantages
which can be summed up as follows :
As we are working with handwritten bengali
text , it is not definite that we will have
straight lines in the characters, i.e. if someone
writes in italics then we will have bent
lines, and the process will not identify that as
a straight line which it should. Thus this
method fails for such cases.

11. Work in the upcoming days
Due to the above demerits in the pre
discussed methods we are now thinking of a
new method . First of all standardization of
the characters is to be done ..so this will give
us a standard sample set which will probably
overcome all the disadvantages previously
mentioned.

12. •
THANK YOU !!!!!