Abstract: This technology is using different fields including

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Abstract: Writing is the painting of the voice and
handwriting enables civilization. All of
us have different handwritings. It is difficult to recognize the different kind
of handwritings, especially the doctor’s prescription. Often the same medicine
is prescribed for different kinds of diseases. The aim of this paper is to
propose a system which use curvelet transform and artificial neural network for
the recognition of doctor’s prescription and convert it into a record. then retrieve
the content  of the medicine. The input
is a scanned image of prescription and output is a bill with the prescription
and the content of medicine. The Curvelet transform is to be used in the
feature extraction stage and artificial neural network is used for recognizing
prescription. Curvelet transform makes it easier to extract curves in handwriting.
Back propagation algorithm is employed to train the system. So this system
helps us to know whether the prescribed medicine is right. It also gives a
solution to the difficulties in understanding a prescription.


Keywords: OCR, ANN, Feature extraction


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1. Introduction

Every individual have different kind of handwriting.
Some handwritings are beautiful and some are not. It is easy for human beings
to read and understand a handwritten document. But a system cannot recognize
the different kind of handwriting. By using the OCR is able to provide that
ability to the system. It is easy for human beings to read and understand a
handwritten document. By using the OCR is able to provide that ability to the
system. The conversion of hand written image or text in to document or record
is called optical character recognition (OCR). Handwriting recognition refers
to understanding or determining the written word and converting it into a
printed format. This technology is using different fields including banking,
postal, teaching etc.

is classified into two types. They are handwritten character recognition and
printed character recognition. Hand written character is again divided in to
two on-line and of-line character recognition. There are several advantage for
OCR. It can reduce the data entry time. It can reduce the storage space
required by the time. The other advantage is fast retrieval of the data.

are many recognition systems to recognize the English handwritten document.
This paper focus on the recognition of a prescription it is very tough to
understand the matter in it. The prescription will be written in cursive
writing. It also will have many curves in it. 
So by using curvelet transform we can easily extract the features of
character. Artificial neural network (ANN) is used for the classification. ANN
is a computational model. The aim of ANN is to provide the human intelligence
to the machines. After classifying the character the system aims to retrieve
the content of the medicine.

2. Related works

There exist many system for the
classifying handwritten characters. Most of the systems does not support the
cursive letters. A handwritten recognition system must have 2 steps. They are feature
extraction and classification. The systems mainly make use of wavelet transform
for the feature extraction. Different algorithms from neural network is used
for the classification. In some system support vector machine is used for the
classification. Many researchers have developed
the character recognition systems by using template matching, spatial features,
Fourier and shape descriptors, Normalized chain code, Invariant moments, central
moments, Zernike moments, modified invariantmoments,structural,statistical,Topological,Gabor,Zoning
features combinations of these feature etc. Different pattern classifiers like
neural networks, Hidden Markov models, and Fuzzy and SVM classifiers are used.


Proposed system


In this
paper we propose a system to recognize the medical prescription and retrieve
the content of the medicine. The system includes 5 modules. The first for
module is for the recognition of the prescription. The last module is to
retrieve the content of the medicine. Modules for handwriting recognition
include preprocessing, segmentation, feature extraction, and classification.
Classification is done using artificial neural network. A neural network is
trained with the 26 characters of English language. The features of the
character which is to be recognized is given as input to the system. The neural
network compares the input features with the trained data set in it. After
classifying or recognizing the letter it returns the letter. After classifying
the entire medicine it is given as an input to medical database. Medical
database returns t1e information on content of the medicine. The modules
implemented in this paper is shown in the fig 1.The proposed system
architecture is shown in fig 2.

Fig 1:

Fig 2:

Image Acquisition

Collection of sample data for training the neural
network is involved in this module. Data from different sources are collected
and stored in a file. The recognition system acquires a scanned image as
an input image. The image should have a specific format such as JPEG, BMT etc.


There will be many irregularities in the scanned
prescription due to the sporadic handwriting. So the scanned image cannot give
directly to the system as input. The irregularities affect the performance of
recognition system badly. So some operations should be performed on the image
to remove their irregularities and to make them in a normalized form.
Preprocessing is done to remove this kind of irregularities in order to get a
better performance. Preprocessing include three functionalities. They are

·       Noise

·       Binarization

·       Thinning

Firstly the cropping of images were done manually.
Then the size of all images is made as uniform. Then the noises form the image
is removed by using median filtering algorithm. Secondly the process of
binarization is done which makes our image as a binary image. It is done by
using Otsu’s
global thresholding method. Now the image is reduced to level intensities white
and black. After inverting the image the boundary box is created for every
words which touches the four sides of the word. At last thinning is done to
resize the image.

3.3. Segmentation

Image segmentation is a process of separating the
image in the super pixels. Segmentation makes the image more meaningful. It is
easy to analyses a segmented image. The scanned prescription contain the names
of medicine. The name is separated in to a single character for further
proceedings. The individual character is obtained by the character

Feature extraction

Feature extraction is used to reduce the dimensionality
of the image.it is done to extract the unique features or property of every
single character in the prescription. By extracting the unique features we can
define a letter with minimum amount of resources. The letter can be represented
with lesser number of bits. curvelet transform is used for the feature
extraction because prescription contain many curves in it. More focus is made
on Discrete
Curvelet Transform with the Wrapping Technique.

Algorithm for Feature Extraction

Input: image after segmentation

Output: features library

segmented image of 64X64 pixels

image is reduced by using a discrete curvelet transform with a wrapping
based technique  

find out the curvelet coefficient for every characters  

4: compute the standard deviation
of these coefficients in order to get a feature set of input

5: obtain the features of every
single character in the image and store it in a train library.


Classification refers to the recognition of the
character.it is done by using a multi-layer perceptron. Neural network is used
for recognition. Before applying neural network it has to be trained with
character database. The input to the trained neural network is the features of
the character that is to be recognized. Neural network is already trained with
26 characters and its features.it compares the input with this data and return
the most matched pattern as the result. The neural network classify the input
into one of the 26 characters.

Algorithm for classification

Input: Isolated test character images.

Output: recognition of prescription

1. Obtain the features as per the algorithm.

2. Store these feature vectors in test library   database.

3. Compute the % of similarity between the features in the test library and
train library.

4. Obtain the character with maximum % of similarity and print that

Obtaining the details of the medicine

After recognizing the letter next step is to
retrieve the content of medicine. For this a medical database is created. The
recognized medicine is given as input to the medical database. It compares with
medicine and the content of medicine to the user.


An algorithm proposed here is used for the
recognition of medical prescription .The system is expected to give a high
performance with the maximum accuracy. Curvelet transform is used for the
feature extraction. It will be easier because the prescription contain many
curves in handwriting. ANN is used to provide the artificial intelligence to
the system. Back propagation algorithm is used to classify the prescription.at
last the text document of the prescription obtained as an output with the
content of medicine in it. This system helps to solve dilemma in understanding
the prescription.

5. Acknowledgment

We hereby express our sincere thanks to our dear teachers and other staffs for their
inestimable and overwhelming support. We would like to express deep sense of gratitude to our guide Ms Anitha L, Asst. professor of
department of computer science and engineering for her encouragement and
guidance for the successful completion of this paper.

We would also like to express our heartfelt thanks to
our beloved parents and friends for their blessings and moral support.


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Parameshwarppa.and V. Dhandra “Handwritten
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5 R. Abd. Rahim1, L.U.Abdul Khalik, M.N.S.Zainudin
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Mangesh A, Navale Ganesh D , Sharma Hemant P, Nikam Krushna V, Shaikh Riyaj R,
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Manuel, Associate Professor, and Saidas S.R. ” Handwritten Malayalam Character Recognition using Curvelet Transform and
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Categories: Architecture


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