Skip to content

Latest commit

 

History

History
85 lines (59 loc) · 1.79 KB

README.md

File metadata and controls

85 lines (59 loc) · 1.79 KB
Raw

Text-Language-Detection-in-Image

Detects and Recognizes text and font language in an image

Description

Performed this analysis using The Tesseract OCR Engine.

The Project consist of following steps :

1.) The first step is a connected component analysis in which outlines of the components are stored into Blobs
2.) Blobs are organized into text lines and broken into words
3.) Recognize every word in a two-pass process
4.) A final phase resolves fuzzy spaces, and finalize text

Prerequisites

Software

  • libtesseract (>=3.04)
  • libleptonica (>=1.71)
  • Cython
  • Pillow
  • tesserocr
  • Python 2.7.0 |Anaconda 4.3.0 (64-bit)|

Tested on Ubuntu 16.04 LTS amd64 xenial image built on 2017-09-19 8-core CPU

Installation

sudo apt-get update -y
sudo apt-get upgrade -y
sudo apt-get install python-dev python-pip
sudo apt-get install tesseract-ocr-all libtesseract-dev libleptonica-dev
pip install Cython
pip install Pillow
pip install tesserocr

Running

  • Simply Clone the repository and run this command from root directory.
python ocr_itt.py -i <image_path.jpg>

Input 1

$ python ocr_itt.py -i e.jpg
e

Output

English
Confidence score is 78.6614583333

Input 2

$ python ocr_itt.py -i h.jpg
h

Output

Hindi
Confidence score is 84.2118644068

Input 3

$ python ocr_itt.py -i s.jpg
s

Output

Spanish
Confidence score is 69.7443609023

Author

Jai Janyani