Admittedly, because of how busy doctors are nowadays, they tend to scribble unreadable prescribed medicines to their patients which leads to the problem of misinterpreting medicine names by pharmacists and patients also. Also, patients are sometimes curious to know information about their prescribed medicines before purchasing them. Recently, developers have been searching for a method to address this problem efficiently but there is no technique leads to full recognition of medicine names due to the bad hand writings of doctors and its variety so that’s leads us to machine learning where the system will learn various types of hand writings for the same medicine in order to be able to recognize new hand writings by comparing them to the trained hand writings. The system presents a solution for both the pharmacist and the patient through a mobile application that recognizes handwritten medicine names and returns a readable digital text of the medicine and its dose. The System identify the medicines names and the doses for the collected data set with some pre-processing techniques like image subtraction, noise reduction and image resizing . After that, the pre-processed images will undergo some processing as it will be classified and feature extracted through Convolutional Neural Network (CNN)and finally Optical Character Recognition (OCR) technique applied on the medicines with low accuracy in the post processing phase to identify their names by comparing the result with data set contains all the medicines. This will help in diminishing the instances of distortion of medication names and will assist drug specialists with limiting their doubts when selling to the patient. The proposed system tested on different real cases and accuracy has reached 70% using (CNN) model .

The objective is to To build a usable mobile application through which, the medical prescription will be first scanned by the mobile camera, then it will go through some pre-processing operations like image subtraction, black white conversion and noise reduction and image resizing. After that, it will go through the processing phase where feature extraction and classification for training the collected data set will be applied using the Convolutional Neural Network (CNN). Finally, the testing part will take place by comparing the output with 20% of the collected data set. OCR technique applied on the medicines with low accuracy to identify their names by comparing the result with data set contains all the medicines.

The proposed mobile app is meant to help the pharmacist read the medical prescription as efficiently as possible. Not only will it facilitate the pharmacist’s job as he will scan the medical prescription, get the photo of the medicine , a text of its dosage on screen and whether it is available or not , rather than struggling with reading the medical prescription and searching for the medicine only to find that it might not be available in the pharmacy, but also, it will save him a lot of time. The objective of the application will be achieved through three stages

• Firstly ,the pre-processing phase consisting of image normalization, morphological operation and Image cropping.

• Secondly, the processing phase consists of: Feature extraction ,and classification

• Thirdly, the post processing phase . Medical prescriptions should be scanned by a camera phone with high camera resolution, photos should be taken from an appropriate and zoomed in angle, to ensure that the scanned image is clear and it’s content is visible .

These conditions if not met, may limit the quality of execution. After the medical prescription is scanned and recognized, results will appear to both the pharmacist and the user, however, each with a different course of action. The results will appear to the user in a form of a digital text , in which he can save it and view information on the resulted medicine. However, the pharmacist will be able to edit the resulted medicine name if it’s recognized incorrectly and if it’s not recognized at all, he will be able to train the system and enter the medicine name by himself and save for future usage