https://github.com/praadnya/govt-circular-analysis
Uses OCR and NER techniques for parsing Goverment Circulars
https://github.com/praadnya/govt-circular-analysis
annotations graphdb ner ocr spacy
Last synced: 2 months ago
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Uses OCR and NER techniques for parsing Goverment Circulars
- Host: GitHub
- URL: https://github.com/praadnya/govt-circular-analysis
- Owner: Praadnya
- Created: 2024-08-21T03:42:39.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-21T11:18:59.000Z (almost 2 years ago)
- Last Synced: 2025-10-08T23:11:48.629Z (10 months ago)
- Topics: annotations, graphdb, ner, ocr, spacy
- Language: Python
- Homepage:
- Size: 77.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Document Processing and Analysis Project
This project involves processing and analyzing documents using Optical Character Recognition (OCR), Named Entity Recognition (NER), and graph databases. The core components include data annotation, model training, OCR extraction, NER processing, and storing data in a Neo4j graph database.
## Project Components
### 1. Annotation
- **Description:** Annotating data involves labeling text in documents to train models for tasks like Named Entity Recognition.
- **Tools Used:** SpaCy for creating and managing annotations.
- **Why:** Accurate annotations are crucial for training effective NER models. The annotations help in recognizing entities and understanding the context within the text.
### 2. Training
- **Description:** Training machine learning models using annotated data to perform NER.
- **Tools Used:** TensorFlow/Keras or PyTorch for model development.
- **Why:** These frameworks provide robust tools for building and training deep learning models, crucial for achieving high accuracy in NER tasks.
### 3. OCR (Optical Character Recognition)
- **Description:** Extracting text from images or PDFs using OCR technology.
- **Tools Used:** Tesseract OCR.
- **Why:** Tesseract helps in converting scanned or image-based text into machine-readable text, which is essential for processing documents that are not in a text format.
### 4. NER (Named Entity Recognition)
- **Description:** Identifying and classifying entities within text.
- **Tools Used:** SpaCy for performing NER tasks.
- **Why:** NER helps in extracting meaningful entities from text, such as names, dates, and organizations, which are crucial for understanding and organizing document content.
### 5. Neo4j (Graph Database)
- **Description:** Storing and querying relationships between entities in a graph database.
- **Tools Used:** Neo4j.
- **Why:** Neo4j efficiently manages and queries complex relationships, which is essential for analyzing connections between different parts of the document data.
## Setup and Installation
### Prerequisites
- Python 3.x
- pip (Python package installer)
### Install Required Libraries
To set up the environment, install the necessary libraries using pip. Run the following command:
```bash
pip install spacy tensorflow keras torch neo4j tesseract