From Prescription to Voice: A Python Solution to Help Service Elderly and Visually Impaired…

🌈 Abstract

The article discusses how Python, FastAPI, and Google Cloud's Text-to-Speech API can be combined to provide a practical solution for visually impaired patients by transforming prescription labels into easily accessible voice messages. The author provides a step-by-step guide to building and testing the application, which involves leveraging OCR and computer vision to extract text from prescription label images and then converting the text to speech.

🙋 Q&A

[01] Extracting Text from Prescription Label Images

1. What are the key steps involved in extracting text from prescription label images?

• The article outlines the following key steps:
  • Preprocess the image using computer vision techniques like adaptive thresholding to enhance visibility
  • Apply Tesseract-OCR (Optical Character Recognition) to extract the text from the preprocessed image
  • Use regular expressions (REGEX) to parse the extracted text and extract relevant fields like prescription name, dosage, number of refills, and expiry date

2. How does the code handle different types of prescription labels?

• The author mentions that the code should be adaptable to different kinds of prescription labels from different health institutions. If the code is being written for a particular pharmacy, it will only need to handle labels of the same format.

3. What libraries are used for the image processing and text extraction tasks?

• The article uses the following libraries:
  • Pillow for opening and saving the prescription label image file
  • OpenCV for computer vision tasks to process the image
  • Pytesseract for optical character recognition to extract text from the label image

[02] Converting Text to Speech

1. How does the code convert the extracted text to speech?

• The article uses Google's Cloud Text-to-Speech API to convert the extracted text into natural-sounding speech. The speech is then saved as an MP3 file.
• The code defines a text2speech() function in the outils.py module that takes the message text as input and applies the Google Text-to-Speech system to synthesize the speech.

2. What customization options are available for the text-to-speech conversion?

• The article mentions that Google's Text-to-Speech API allows for customization of the language, voice, pitch, and other options.

[03] Building the FastAPI Application

1. How is the FastAPI application structured?

• The FastAPI application is defined in the main.py file and includes the following key components:
  • A POST endpoint /speech_from_doc that accepts an uploaded image file
  • A function speech_from_doc() that processes the uploaded file, extracts the text, and generates the speech output
  • Error handling using a custom CustomException class defined in the exception.py file
  • Logging integration using the logging module

2. How is the application tested and run?

• The article suggests using Postman as the testing tool to upload the prescription label image and receive the speech output.
• The application is run using the Uvicorn server, which is started in the main.py file.