Understanding the Structure: From the images recognize the repeated format for each minister: three columns labeled "Subjects and Functions," "Departments, Statutory Institutions and Public Corporations," and "Laws, Acts, and Ordinances to be Implemented." Each section had numbered lists or bulleted points.
Manual Extraction and Formatting: Manually extract this structured information by interpreting the visible text in each column across images, relying on OpenAI models' trained ability to analyze and organize the content as text.
Organizing Data by Minister: Based on each minister's distinct titles (e.g., "Minister of Defence," "Minister of Finance..."), Group content accordingly and then organize each part into a three-column format, maintaining the original list structure from the images.
Reformatting for Readability: Present the structured information in a clear, numbered, and bulleted format to make it easier to read and follow.
If these were to be performed programmatically, it would involve more steps than the previous code provided, and it would likely require some manual post-processing to ensure accuracy, given that OCR might introduce errors or inconsistencies.
What Would Be Needed to Automate This Process
To automate a task like this as closely as possible to how I initially presented it, a more advanced approach would be necessary. Here's what that would entail:
OCR for Text Extraction: Using Tesseract or similar OCR software to extract text from images.
Pattern Recognition with NLP: Leveraging natural language processing (NLP) techniques or AI models (like GPT) to recognize and parse complex structures, lists, and sections in the text. This would include detecting titles like "Minister of Defence" and identifying related sections.
Error Handling and Post-Processing: Ensuring accuracy in the OCR results and addressing any misreads or inconsistencies, possibly with manual review.
Steps to Improve the Accuracy of the workflow
Understanding the Structure: From the images recognize the repeated format for each minister: three columns labeled "Subjects and Functions," "Departments, Statutory Institutions and Public Corporations," and "Laws, Acts, and Ordinances to be Implemented." Each section had numbered lists or bulleted points.
Manual Extraction and Formatting: Manually extract this structured information by interpreting the visible text in each column across images, relying on OpenAI models' trained ability to analyze and organize the content as text.
Organizing Data by Minister: Based on each minister's distinct titles (e.g., "Minister of Defence," "Minister of Finance..."), Group content accordingly and then organize each part into a three-column format, maintaining the original list structure from the images.
Reformatting for Readability: Present the structured information in a clear, numbered, and bulleted format to make it easier to read and follow.
If these were to be performed programmatically, it would involve more steps than the previous code provided, and it would likely require some manual post-processing to ensure accuracy, given that OCR might introduce errors or inconsistencies.
What Would Be Needed to Automate This Process
To automate a task like this as closely as possible to how I initially presented it, a more advanced approach would be necessary. Here's what that would entail: