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The document outlines the steps to design a text summarizer for extracting specific information from PDF or JPG files, including defining goals, creating a processing pipeline, and utilizing various tools and libraries. Key steps include input handling, text extraction using OCR, information extraction through Named Entity Recognition, and optional summarization methods. It also provides an example use case for extracting names and dates from images and suggests building a GUI for user interaction.

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Abhishek Agrawal
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10 views3 pages

Project

The document outlines the steps to design a text summarizer for extracting specific information from PDF or JPG files, including defining goals, creating a processing pipeline, and utilizing various tools and libraries. Key steps include input handling, text extraction using OCR, information extraction through Named Entity Recognition, and optional summarization methods. It also provides an example use case for extracting names and dates from images and suggests building a GUI for user interaction.

Uploaded by

Abhishek Agrawal
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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Designing a text summarizer to extract specific information from a PDF or JPG involves a few

key steps. Here's a breakdown of how you could approach it:

🧠 Step 1: Define the Goal

Clarify what specific information you're trying to extract. For example:

 Key entities (names, dates, organizations)

 Answers to specific questions

 Summaries of sections (e.g., patient info, invoice total, legal clauses)

🧩 Step 2: Pipeline Overview

Your summarizer pipeline will likely look like this:

1. Input handling:

o Accept PDF or JPG.

2. Text extraction:

o OCR for JPG (and sometimes PDF if scanned).

o Text parsing for digital PDFs.

3. Information extraction:

o Named Entity Recognition (NER)

o Rule-based or AI-based extractors.

4. Summarization (optional):

o Extractive or abstractive summarization.

5. Output:

o JSON, CSV, text, or GUI output.

🔧 Step 3: Tools & Libraries

📄 PDF/JPG to Text
 PDF:

o Use PyMuPDF, pdfplumber, or pdfminer.six for text-based PDFs.

o Use OCR (Tesseract, EasyOCR) for scanned/image PDFs.

 JPG:

o Use OCR:

o import pytesseract

o from PIL import Image

o image = Image.open("sample.jpg")

o text = pytesseract.image_to_string(image)

🧠 Text Processing & Information Extraction

 Spacy or transformers (HuggingFace) for NER.

 Custom regex for known patterns (e.g., invoice number, date).

 LangChain / LLMs (like GPT-4 or Claude) if natural language queries are needed.

📝 Summarization

 Extractive (e.g., sumy, gensim)

 Abstractive (e.g., transformers with T5, BART, or GPT models)

💡 Example Use Case: Extract Name and Date from JPG

import pytesseract

from PIL import Image

import re

def extract_name_date(image_path):

text = pytesseract.image_to_string(Image.open(image_path))
name_match = re.search(r"Name:?\s*([A-Z][a-z]+\s+[A-Z][a-z]+)", text)

date_match = re.search(r"Date:?\s*(\d{2}/\d{2}/\d{4})", text)

return {

"name": name_match.group(1) if name_match else None,

"date": date_match.group(1) if date_match else None,

"raw_text": text

🚀 Optional: Make It Smarter with LLMs

Use a language model (like GPT or a local LLM) to extract and summarize:

from transformers import pipeline

summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

summary = summarizer(long_text, max_length=100, min_length=30, do_sample=False)

print(summary[0]['summary_text'])

📁 Want a GUI?

You could build a simple UI with:

 Streamlit

 Gradio

 Flask/Django + HTML

If you tell me the specific info you want to extract, I can help build a working prototype or code
template for that

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