What is Eco_Bill Optimizer and Why it is ?
The motivation behind developing this code lies in the growing need for efficient energy management and cost optimization in both residential and commercial settings. In an era where energy consumption directly impacts not only financial expenses but also environmental sustainability, understanding and managing electricity usage has never been more crucial. By automating the extraction and analysis of data from electricity bills, this code empowers users to gain detailed insights into their energy consumption patterns, billing inconsistencies, and potential areas for cost-saving.
With the ability to analyze historical billing data, consumers and businesses can identify trends such as peak usage times, excessive charges, or discrepancies in billing. This level of analysis enables informed decision-making, allowing users to adopt energy-saving measures, optimize their electricity usage, and potentially switch to more cost-effective energy plans. Moreover, by visualizing this data through platforms like Power BI and Tableau, users can easily monitor and track their progress in reducing energy consumption, ultimately contributing to both financial savings and a smaller carbon footprint. The code provided is designed to automate the extraction of text from images, specifically bill documents stored in a Google Drive folder, and then process that text to extract relevant fields related to electricity billing. This workflow is particularly useful for creating structured datasets that can be further analyzed using tools like Power BI and Tableau.
Tech Stack:
Libraries Used: Pytesseract: This is a Python wrapper for Google's Tesseract-OCR engine. It is used for optical character recognition (OCR), which allows the extraction of text from images. Pytesseract is essential for converting the textual content within scanned documents or image files into a machine-readable format.
PIL (Python Imaging Library) / Pillow: This library is used for opening, manipulating, and saving image files in various formats. In this code, it works alongside Pytesseract to handle the image files from which text is extracted.
PyMuPDF (fitz): PyMuPDF is a Python binding for MuPDF, a lightweight PDF viewer, and toolkit. It's used in this code to process PDF documents, extracting text either directly or through OCR when the text is embedded in images within the PDF.
Pandas: A powerful data manipulation and analysis library, Pandas is used to handle and process structured data. In this code, Pandas is utilized for loading, processing, and saving the extracted text data into a CSV file format, which is a common data structure for further analysis.
Regular Expressions (re): The re module in Python is used for matching and searching patterns in text. This is crucial for extracting specific fields from the raw OCR output, such as meter numbers, bill dates, and charges, ensuring the data is clean and structured.
CSV: This module is used for reading from and writing to CSV (Comma Separated Values) files. In this code, it facilitates the storage of extracted text data in a structured format that is easy to import into data analysis tools like Power BI and Tableau.
Sweetviz: A Python library for creating visualized, high-density EDA (Exploratory Data Analysis) reports with just a few lines of code. It compares datasets, highlights key insights, and generates interactive HTML reports.
Google Colab and Google Drive Integration: The code is designed to run on Google Colab, a cloud-based platform that provides free access to GPUs and TPUs for machine learning tasks. Google Drive integration allows easy access to datasets stored in the cloud, making the process seamless and scalable.
Tech Stack: Python: The primary programming language used for writing the code. Python's versatility and extensive library support make it ideal for handling image processing, OCR, data extraction, and manipulation tasks.
Google Colab: A cloud-based platform that provides an interactive environment for writing and executing Python code. Colab supports Google Drive integration, allowing users to access and process large datasets stored in the cloud without the need for local storage.
Tesseract-OCR: An open-source OCR engine developed by Google, used for extracting text from images and PDFs. Tesseract-OCR is a key component in the tech stack, enabling the conversion of visual data into text.
Data Visualization Tools (Power BI and Tableau): While not directly part of the code, these tools are intended to be used downstream with the structured data output (CSV files) generated by the code. They are crucial for creating interactive dashboards, reports, and visualizations to analyze the extracted data.
Working model:
The process begins with the installation of necessary libraries such as Tesseract OCR, which is responsible for optical character recognition (OCR) of the text within the images. The Google Colab environment is used, and the Google Drive is mounted to access the images stored in a specified folder. The first part of the code focuses on reading these images, processing each one to extract the text content, and saving this raw extracted text into a CSV file. Each image in the folder is read using the Python Imaging Library (PIL), and the text is extracted using Tesseract OCR. The results are stored in a structured CSV format, with each row representing a document and containing the file name and the extracted text.
Link to dataset - https://drive.google.com/drive/folders/1kKUqWCOLKY7g_NGy1xJI4qh7_noA2Cqs?usp=drive_link
Once the raw text data is saved, the second part of the code comes into play. This portion of the script is responsible for post-processing the extracted text to correct common OCR errors and to identify specific fields relevant to electricity billing, such as meter number, bill number, bill date, and various charges. Regular expressions are used to accurately locate and extract these fields from the text, ensuring that the data is organized in a structured manner. Corrections are applied to the text to address common OCR misinterpretations, improving the accuracy of the extracted data.
After the relevant fields are extracted and corrected, the processed data is saved into a new CSV file. This file now contains a structured dataset with columns for each relevant field, making it ready for analysis. The output is a clean, structured dataset that can be directly imported into data visualization tools like Power BI and Tableau. These tools can then be used to create dashboards, reports, and visualizations, providing insights into electricity usage, billing trends, and other key metrics.
This automated approach not only saves time and effort but also ensures a higher level of accuracy and consistency in the data extraction process. The structured dataset produced by the code can serve as a reliable foundation for business intelligence and analytics, allowing users to uncover patterns, trends, and anomalies in electricity billing data. By leveraging this process, organizations can make data-driven decisions, improve their operational efficiency, and enhance their financial analysis capabilities.
In summary, the code provides a comprehensive solution for transforming unstructured image data into a structured format that is ready for advanced analysis. This makes it an invaluable tool for organizations looking to optimize their data processing workflows and gain deeper insights into their operations through sophisticated data visualization and analysis tools.