A deep learning algorithm is proposed to analyze doctor handwritten prescriptions by tracing the writing to clarify medication names, purposes, dosages, and timings. The algorithm would collect a large, diverse dataset of prescription images for training, preprocess the images, segment the handwriting into lines, recognize the writing using CNNs and RNNs, post-process the output, and deploy the model in an application to digitize prescription texts from uploaded images.

1. Get clarity on doctor
handwritten prescriptions
Using Deep learning Algorithms
Statement:
“As doctor handwritten prescription is hard to understand, create a DL algorithm which can
trace the written prescription and gives clarity about medicine name, purpose of using it,
timings to use it with dosage.”
PRESENTED BY:
S.NAGA SANDEEP[21G21A31G2]
S.V.RISHIK[21G21A31G4]
B.BHARATH[21G21A0511]

2. • SOLUTION:
• Tracing doctor handwriting in a prescription is a challenging problem, and it would require a combination of computer vision and
natural language processing techniques. Here is a possible approach for building a deep learning algorithm to solve this problem:
• 1. Data Collection: Collect a large dataset of prescription images with doctor handwriting. This dataset can be obtained by
collaborating with healthcare organizations or by web scraping. It is important to ensure that the dataset is diverse enough to
cover a wide range of handwriting styles and formats.
• 2. Image Preprocessing: The prescription images need to be preprocessed to remove any noise or artifacts that could interfere
with the handwriting recognition process. This can involve techniques such as image binarization, noise reduction, and skew
correction.
• 3. Handwriting Segmentation: Next, the prescription image needs to be segmented into individual lines of handwriting. This can
be achieved using techniques such as connected component analysis or line segmentation.
• 4. Handwriting Recognition: Once the handwriting has been segmented, it can be fed into a deep learning model for recognition.
There are several different deep learning architectures that can be used for handwriting recognition, including Convolutional
Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). One possible approach is to use a combination of CNNs and
RNNs in a sequence-to-sequence (seq2seq) model.
• 5. Post-Processing: After the handwriting has been recognized, the output needs to be post-processed to ensure accuracy and
consistency. This can involve techniques such as error correction and normalization.
• 6. Deployment: The final step is to deploy the deep learning model in a user-friendly interface, such as a mobile application or
web-based tool, that allows doctors to upload images of prescriptions and receive digitized versions of the prescription text.
DOCTOR PRESCRIPTION
RECOGNITION
ALGORITHM

3. PROCESS FLOW
The study implements the
following approach that has
four (4) stages as shown in
Fig. 1 namely Data Collection,
Pre- processing, Training of
Data and Model Evaluation
and Analysis