Abstract: data. There is a lot of recognition

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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 type
of handwritings, especially a doctor’s prescription. Sometimes 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 text document
initially. Next, to retrieve the content medicine. The input is a scanned image
of prescription and output is a bill with text document of prescription and the
details of medicine.  The Curvelet
transform is to be used in the feature extraction stage and artificial neural
network is used for classification and recognition of character. Curvelet
transform helps in extracting curves in handwriting more accurately. Back
propagation algorithm is used 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.


Every individual have different type of handwriting.
Some have very beautiful and some have the worst one. 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 optical character recognition is a vast growing area
in the field of computer science. The conversion of hand written image or text
in to ta computer readable or printed format 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 in many fields like postal, banking, etc.

is divided 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.

is a lot of recognition system 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 running
letters. 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.

Related works

There exist several system for the
recognition of handwritten character recognition. Most of the system are for
single characters. A handwritten recognition system must have 2 steps. They are
feature extraction and classification. The existing system 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.

                    There exist very few system
to recognize the running letters in English and a medical prescription.one
reason is complexity to understand different handwriting and legibility of




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:


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.


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 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. A prescription contain many
curves along with the lines, so curvelet transform is used for the feature
extraction. 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

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 difficulties in
understanding the prescription.


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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Categories: Architecture


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