Text Analysis (TA) is a process which takes unseen texts as input and produces fixed-format, unambiguous data as output. This data may be used directly for display to users, or may be stored in a database or spreadsheet for later analysis, or may be used for indexing purposes in Information Retrieval (IR) applications.

1. Applying OCR to Extract Information: Text-Mining
Step 1:
Get Access of Scanned
PDF Documents
Step 2:
Use of Apache Tika
library to extract
textual data
Step 3:
Information extraction
from text to structured
tables
Data processing Steps:

2. Applying OCR to Extract Information: Text-Mining
Step 1: Access Scanned PDF documents
Extracting/Connecting data to Hadoop
server to get access of scanned PDF files
(document) in Python Environment.
Step 2: Text Extraction
Use of parser from Apache Tika library to
extract text from each assessment orders
and store in a table form with two columns
namely "Assessment Order ID" and "Actual
Text".
Synopsis of text extracted into a table:
Step 3: Information Extraction from text
Extracting following list of information with the use of Regular Expressions (pattern search) over Actual Text for
each document.
1) Name
2) Financial Year
3) PAN
4) Legal Citation (which includes citation of SC, HC & ITAT) and
5) Legal Issues associated with each document

3. 1.Define
2.Design
3.Deploy
4.Analyze
5.Act
Define:
Identify specific requirements within use cases while highlighting risk factors
and estimate value opportunities
Design:
Design a tracking strategy that captures the appropriate data with proper
KPIs of the business requirement
Deploy:
Implement the technologies required to capture the data as along with the
measurement strategy design
Analyze:
Insight driven analyses to expose challenges and identify opportunities
Act:
Leverage Analysis to describe and prescribe the challenges with solutions
and uncover the hidden opportunities
Analytics Cycle
Text Analytics
Text Analysis (TA) is a process which takes unseen texts as input and produces fixed-format, unambiguous data as output.
This data may be used directly for display to users, or may be stored in a database or spreadsheet for later analysis, or may be
used for indexing purposes in Information Retrieval (IR) applications.

4. Documents
• Text Mining
• Topic Modeling
• Text Classification
• Named Entity Recognition
• Relation extraction
• Event detection
• Natural Language Toolkit (NLTK)
• Gensim,
• Scikit-Learn
Multi-dimensional Text Mining Tools
Word Frequency Analysis:
• Most Frequent words
• Frequency Distribution
Results
Text Classification:
• Multi-label Domain Specific
classified texts
Collocation Analysis
• Bigrams
• Trigrams and
• N-grams
Keyword Analysis
• Keyword Counts,
• Most prominent Categories
Topic Modeling
• Discovering Topics and
Categories
Performance Measures
• Accuracy, Precision, Recall,
F-Measures
Comprehensive tool set for
• Data editing and visualization
• Rapid application development
• Manual annotation
• Ontology management
User Interface
Text Analytics - Process Flow