Abstract: Writing is thepainting of the voice and handwriting enables civilization. All of us have different handwritings.It is difficult to recognize the different kind of handwritings, especially thedoctor’s prescription. Often the same medicine are prescribed for differentkinds of diseases. The aim of this paper is to propose a system which usescurvelet transform and artificial neural network for the recognition ofdoctor’s prescription and convert it into a record.
Then retrieve the content ofthe medicine. The input is a scanned image of prescription and output is a billwith the prescription and the content of medicine. The Curvelet transform is tobe used in the feature extraction stage and artificial neural network is usedfor recognizing prescription. Curvelet transform makes it easier to extractcurves in handwriting. Back propagation algorithm is employed to train thesystem.
So this system helps us to know whether the prescribed medicine isright. It also gives a solution to the difficulties in understanding aprescription. Keywords:OCR, ANN, Feature extraction and Classification1. Introduction Every individual has different kind of handwriting.Some handwritings are beautiful and some are not. It is easy for human beingsto read and understand a handwritten document. But a system cannot recognizethe different kind of handwriting.
By using the OCR is able to provide thatability to the system. It is easy for human beings to read and understand ahandwritten document. By using the OCR is able to provide that ability to thesystem. The conversion of hand written image or text in to document or recordis called optical character recognition (OCR).
Handwriting recognition refersto understanding or determining the written word and converting it into aprinted format. This technology is using different fields including banking,postal, teaching etc. OCRis classified into two types.
They are handwritten character recognition andprinted character recognition. Hand written character is again divided in totwo on-line and of-line character recognition. There are several advantages forOCR. It can reduce the data entry time. It can reduce the storage spacerequired by the time. The other advantage is fast retrieval of the data.
Thereare many recognition systems to recognize the English handwritten document.This paper focus on the recognition of a prescription it is very tough tounderstand the matter in it. The prescription will be written in cursivewriting. It also will have many curves in it. So by using curvelet transform we can easily extract the features ofcharacter. Artificial neural network (ANN) is used for the classification. ANNis a computational model.
The aim of ANN is to provide the human intelligenceto the machines. After classifying the character the system aims to retrievethe content of the medicine. 2. Related works There exist many systems for theclassifying handwritten characters. Most of the systems do not support thecursive letters. A handwritten recognition system must have 2 steps. They are featureextraction and classification.
The systems mainly make use of wavelet transformfor the feature extraction. Different algorithms from neural network are usedfor the classification. In some system support vector machine is used for theclassification.
Many researchers have developedthe character recognition systems by using template matching, spatial features,Fourier and shape descriptors, Normalized chain code, Invariant moments, centralmoments, Zernike moments, modified invariantmoments,structural,statistical,Topological,Gabor,Zoningfeatures combinations of these features etc. Different pattern classifiers likeneural networks, Hidden Markov models, and Fuzzy and SVM classifiers are used. 3.Proposed Methodologies In thispaper we propose a system to recognize the medical prescription and retrievethe content of the medicine.
The system includes 5 modules. The first four modulesare for the recognition of the prescription. The last step is to retrieve thecontent of the medicine. Modules for classifying the prescription includepreprocessing, segmentation, feature extraction, and classification.
Classification is done using artificial neural network. A neural network istrained with the 26 characters of English language. The features of the characterwhich is to be recognized are given as input to the system. The neural networkcompares the input features with the trained data set in it.
After classifyingor recognizing the letter it returns the letter. After classifying the entiremedicine it is given as an input to medical database. Medical database returnst1e information on content of the medicine. The modules implemented in thispaper are shown in the fig 1.
The proposed system architecture is shown in fig2.Fig 1:Fig 2: 3.1.Image Acquisition Collection of sample data for training the neuralnetwork is involved in this module. Data from different sources are collectedand stored in a file.
The recognition system acquires a scanned image asan input image. The image should have a specific format such as JPEG, BMT etc. 3.2.
Preprocessing There will be many irregularities in the scannedprescription due to the sporadic handwriting. So the scanned image cannot givedirectly to the system as input. The irregularities affect the performance ofrecognition system badly. So some operations should be performed on the imageto remove their irregularities and to make them in a normalized form.Preprocessing is done to remove this kind of irregularities in order to get abetter performance.
Preprocessing includes three functionalities. They are· Noiseremoval· Binarization· ThinningFirstly the cropping of images were done manually.Then the size of all images is made as uniform. Then the noises form the imageis removed by using median filtering algorithm. Secondly the process ofbinarization is done which makes our image as a binary image. It is done byusing Otsu’sglobal thresholding method.
Now the image is reduced to level intensities whiteand black. After inverting the image the boundary box is created for every wordwhich touch 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 theimage in the super pixels. Segmentation makes the image more meaningful. It iseasy to analyses a segmented image.
The scanned prescription contains the namesof medicine. The name is separated in to a single character for furtherproceedings. The individual character is obtained by the charactersegmentation. 3.4.
Feature extraction Feature extraction is used to reduce thedimensionality of the image.it is done to extract the unique features orproperty of every single character in the prescription. By extracting theunique features we can define a letter with minimum amount of resources.
Theletter can be represented with lesser number of bits. Curvelet transform isused for the feature extraction because prescription contains many curves init. More focus is made on DiscreteCurvelet Transform with the Wrapping Technique. Algorithm for Feature Extraction Input: image after segmentationOutput: features library1: segmented image of 64X64 pixels 2: image is reduced by using a discrete curvelet transform with a wrappingbased technique 3: find out the curvelet coefficient for every characters 4: compute the standard deviationof these coefficients in order to get a feature set of input5: obtain the features of everysingle character in the image and store it on a train library. 3.5.Classification Classification refers to the recognition of thecharacter.
it is done by using a multi-layer perceptron. Neural network is usedfor recognition. Before applying neural network it has to be trained withcharacter database. The input to the trained neural network is the features ofthe character that is to be recognized. Neural network is already trained with26 characters and its features.it compares the input with this data and returnthe most matched pattern as the result. The neural network classifies the inputinto one of the 26 characters.
Algorithm for classification Input: Isolated test character images. Output: recognition of prescription1. 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 andtrain library.4. Obtain the character with maximum % of similarity and print that character.3.6Obtaining the details of the medicine After recognizing the letter next step is toretrieve the content of medicine. For this a medical database is created. Therecognized medicine is given as input to the medical database.
It compares withmedicine and the content of medicine to the user. 4Conclusion An algorithm proposed here is used for therecognition of medical prescription .The system is expected to give a highperformance with the maximum accuracy. Curvelet transform is used for the featureextraction. It will be easier because the prescription contains many curves inhandwriting. ANN is used to provide the artificial intelligence to the system.Back propagation algorithm is used to classify the prescription.at last thetext document of the prescription obtained as an output with the content ofmedicine in it.
This system helps to solve the dilemma in understanding the prescription. 5. Acknowledgment We hereby express our sincere thanks to ourdear teachers and other staffs fortheir inestimable and overwhelming support. We would like to express deep sense of gratitude to our guide Ms Anitha L, Asst. professor ofdepartment of computer science and engineering for her encouragement andguidance for the successful completion of this paper.We would also like to express our heartfelt thanks toour beloved parents and friends for their blessings and moral support.
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