Tamil OCR using Tesseract OCR Engine

 NAME : K.Balamurugan
 REG NO : 12370002
 COURSE : M.SC (Comp. Science)
 SEMESTER : IV Semester
Under Guidance
Dr.K.S. Kuppusamy
TAMIL
(TAMIL OCR USING MULTIDIMENSIONAL INTERACTIVE LEARNING MODEL)

Introduction to OCR
Abstract
Software Requirement
Analysis
Design
GUI Design
Conclusion
Outline:

What is OCR?
 Optical Character
Recognition, usually
abbreviated to OCR, is
the conversion of scanned
photo/ Images of typewritten
or printed text into machine-
encoded/computer-readable
text.
Introduction to OCR

Types of OCR
Basically, there are three types of OCR. They are briefly discussed
below:
 Handwritten Text OCR
The text produced by a person by writing with a pen/ pencil on a paper
medium and which is then scanned into digital format using scanner is
called Handwritten Text.
 Machine Printed Text OCR
Machine printed text can be found commonly in daily use. It is
produced by offset processes, such as laser, inkjet and many more. The
project comes under this category.
Introduction - Type of OCR

 TAMIL (Tamil OCR using Multidimensional Interactive Learning
model) is Optical Character Recognition Software that convert machine
printed text into editable text. It is mainly targeting to Tamil language. It
uses tesseract OCR Engine.
 Tesseract OCR engine has trained in Tamil language so this software can
convert an image in to text in Tamil language ; it involves the Tamil
Tessdata files for mapping of each character in image into text. While trying
to recognize a limited range of fonts (like a single font for instance), then a
single training page might be enough. Tesseract was originally designed to
recognize English text only. Efforts have been made to modify the engine
and its training system to make them able to deal with other languages and
UTF-8 characters.
TAMIL OCR USING MULTIDIMENSIONAL INTERACTIVE
LEARNING MODEL
Abstract

Tesseract-OCR is the most widely used open source OCR
across the world. Currently this OCR supports English language as
default and few more language and it is a command line tool. Tesseract
OCR Engine has flexibility that it can be trained to any language. In this
project an application is developed to train OCR in Tamil languages.
More Training Images and effort are needed to produce the training data.
Since Tesseract-OCR is a command line tool, it is not mostly used by the
beginners which limit the usage of tesseract. Here an excellent GUI was
developed for tesseract-OCR, which makes peoples to use this OCR
easily.
Tamil OCR GUI is multidimensional and interactive. It uses espeak TTS
engine to convert Tamil Text into voice. So it much beneficial for blind
people.
Abstract
TAMIL OCR USING MULTIDIMENSIONAL INTERACTIVE
LEARNING MODEL

 Software Requirements
 Tesseract 3.02 OCR Engine
 JTessBoxEditor
 SerakTesseractTrainer
 Visual studio 2010
 .NET Framework 4.0
 VB.NET
 Notepad ++ (Text Creator)
 Text2Image Command line
 Ghost Script 9.0
 WebsitesScreenshot.dll
 Windows Image Acquisition(Wiaaut.dll)
 Hunspell Spell Checker
 NHunspellExtender
 eSpeak
 espeak Data Files
Requirements

Requirements
 MILE Lab Tamil language Text-To-Speech (TTS) Engine
 Google Transliterations API
 Google Speech Service
 Tamil and English Spell Check Dictionaries
 Microsoft Speech API (SAPI) 5.4 (SAPI)
 Tessdata with Tamil Trained Data file
 Web browser Component
 VC++ Runtime
 Microsoft Office Interoperability Word Service
 Tamil Fonts and Tamil Keyboard software
 Windows Operating system(From windows XP to Windows 8)

Requirements
User Software Requirements
 VC++ Runtime 2010
 .NET Framework 4.0
Hardware Requirements:
 Minimum 260MB RAM memory, preferably 600MB RAM
 700MB free hard disk space
 Modem for internet connection

Software Requirements - Tesseract
OCR Engine
 The Tesseract engine was originally developed as proprietary
software at Hewlett Packard labs in Bristol, England and Greeley,
Colorado between 1985 and 1994, with some more changes made in
1996 to port to Windows, and some migration from C to C++ in
1998. A lot of the code was written in C, and then some more was
written in C++. Since then all the code has been converted to at
least compile with a C++ compiler. Very little work was done in the
following decade. It was then released as open source in 2005 by
Hewlett Packard and the University of Nevada, Las Vegas (UNLV).
Tesseract development has been sponsored by Google since 2006.
 Tesseract does not come with a GUI and is instead run from
the command-line interface.

 Tesseract was in the top three OCR engines in terms of character accuracy in
1995. It is available for Linux, Windows and Mac OS X, however, due to
limited resources only Windows and Ubuntu are rigorously tested by
developers.
 Tesseract up to and including version 2 could only accept TIFF images of
simple one column text as inputs. These early versions did not include layout
analysis and so inputting multi-columned text, images, or equations
produced a garbled output. Since version 3.00 Tesseract has supported
output text formatting, hOCR positional information and page layout
analysis. Support for a number of new image formats was added using the
Leptonica library. Tesseract can detect whether text is monospaced or
proportional.
OCR Engine

OCR Engine
 The initial versions of Tesseract could only recognize English language text.
Starting with version 2 Tesseract was able to process English, French, Italian,
German, Spanish, Brazilian Portuguese and Dutch. Starting with version 3 it
can recognize Arabic, English, Bulgarian, Catalan, Czech, Chinese (Simplified
and Traditional), Danish, German (standard and Fraktur script), Greek,
Finnish, French, Hebrew, Croatian, Hungarian, Indonesian, Italian, Japanese,
Korean, Latvian, Lithuanian, Dutch, Norwegian, Polish, Portuguese,
Romanian, Russian, Slovak (standard and Fraktur script), Slovenian, Spanish,
Serbian, Swedish, Tagalog, Thai, Turkish, Ukrainian and Vietnamese.
Tesseract can be trained to work in other languages too

 Each box results in a component which represents a single
character. The box has an x- and y-coordinate, a width and height
and the character which has been recognized in the region.
 we have to manually edit possible mistakes that has been detected
by Tesseract. Maybe all characters where wrong because the font
you are using isn’t very common. To make this job a lot easier,
several tools are available. These tools are called “Tesseract box
editors”, the tool I used is jTessBoxEditor. jTessBoxEditor is written
and Java and thus platform independent, it has options to merge
and split which could be handy. After you corrected the mistakes,
we can creating the training file.
Software Requirements –
Box Editor

 jTessBoxEditor is a box editor and trainer for Tesseract
OCR, providing editing of box data of both Tesseract 2.0x
and 3.0x formats and full automation of Tesseract
training. It can read images of common image formats,
including multi-page TIFF. The program requires Java
Runtime Environment 6.0 or later.
Software Requirements -
jTessBoxEditor

Text-To-Speech Engine
 Introduction to TTS
 A text-to-speech software or speech synthesis program is used
to read a text out loud to a user. Espeak can be used for this
purpose. A TTS engine uses natural human voices to avoid
sounding like a robot during the reading of a text to a user.
There are also few different free TTS engines available online
where the user can choose the voice he prefers the text to be
read with. One I used is MILE Lab Tamil language Text-To-
Speech Engine.
Software Requirements - TTS

 Espeak
 ESpeak is a compact open source software speech
synthesizer for Linux, Windows, and other platforms. It uses
a formant synthesis method, providing many languages in a small
size and it has also been used by Google Translate.
 ESpeak is derived from the "Speak" speech synthesizer for British
English for Acorn RISC OS a computer which was originally written
in 1995 by Jonathan Duddington. A rewritten version for Linux
appeared in February 2006 and a Windows SAPI 5 version in
January 2007. Subsequent development has added and improved
support for additional languages.
 In my project espeak is used as offline Tamil text to speech engine.
It support multi language speech.
Software Requirements - ESpeak

 Microsoft Speech API (SAPI)
 The Microsoft text-to-speech voices are speech
synthesizers provided for use with applications that
use the Microsoft Speech API (SAPI) or the Microsoft
Speech Server Platform. There are client and server
versions of Microsoft text-to-speech voices. SAPI
used to produce interactive speech feature in the
project
Software Requirements -Microsoft
Speech API (SAPI)

 Hunspell
 Hunspell is a spell checker and morphological analyser designed
for languages with rich morphology and complex word
compounding and character encoding, originally designed for
the Hungarian language.
 Hunspell is based on MySpell and is backward-compatible with
MySpell dictionaries. While MySpell uses a single-byte character
encoding, Hunspell can use Unicode UTF-8-encoded dictionaries.
Hunspell The spell checker Hunspell needs two files:
1. Dictionary files with .DIC extension
2. Affix file with .AFF extension
Software Requirements - Hunspell

 Ghostscript is a suite of software based on an interpreter for
Adobe Systems' PostScript and Portable Document Format
(PDF)page description languages. Its main purposes are the
rasterization or rendering of such page description language
files, for the display or printing of document pages, and the
conversion between PostScript and PDF files.
 GhostScript is open source it is used in the project for
converting PDF files to image. PDF OCR uses this GhostScript.
Software Requirements - GhostScript

 Existing System
The existing tesseract-OCR supports English language as
default and also supports languages like Dutch, Spanish, Italian,
French and German. All these languages are trained to
tesseract OCR, but languages like Tamil, Malayalam are not
much trained to OCR. There are no GUI available for tesseract
in tamil and training tesseract is a big task which an
intermediate persons too feel complex for training it. Since
Tesseract OCR Engine is command line Tool, usage of OCR is
much less.
Analysis

There are some OCR GUI are built using Tesseract OCR Engine, but it
does not have much support for Tamil language.
 Some GUI tools are listed below.
 VietOCR
 Tesseract-OCR QT4 gui
 Lime OCR
 Few Online Services:
 CustomOCR
 Free OCR
 i2OCR(support Tamil language, but very less accuracy)
Analysis-Existing System

The proposed system
The proposed system has to be GUI. GUI of Proposed system is named as
“Tamil OCR GUI”. It is trained to Tamil language, using Tesseract Training
Procedures. It will much beneficial for blind people and normal users.
The inputs to the OCR-Engines are:
 Sample Tamil Training Images
 Data Files
 Tamil Dictionary
 Final Tamil trained data
As I will use the programming language VB.NET, it satisfies all the needs
as extensibility, simplicity, interoperability, portability, powerful data
structures and also Unicode support. The GUI was developed in VB.NET,
which is Threaded application.
Analysis- proposed system

GUI is Web browser And Special OCR GUI in this project and the
it going to capture web page images and extract all its Tamil and
English text using the Tamil OCR component.
Proposed System has support of following language:
 1. Tamil
 2. English
User can switch between these language and they can easily
OCR Tamil and English Images using Proposed System.
Analysis- proposed system

Following are process of Tesseract OCR Engine:
 At the first stage, outlines of the text are gathered by nesting, into Blobs. These blobs are
organized into text lines and are broken into words differently according to the kind of
character spacing. After that the lines and regions are analysed for fixed pitch or proportional
text. Fixed pitch text is chopped immediately by character cells, however the proportional
text is broken into words using definite spaces and fuzzy spaces. Then the recognition
proceeds as a two-pass process.
 In the first pass, it attempt recognize each word in turn passed to an adaptive
classifier as training data. The adaptive classifier then gets a chance to more accurately
recognize text. Thereafter the second pass is run over the page for the words those were
not recognized well enough in the First pass.
 At last a final phase resolves fuzzy spaces, and checks alternative hypotheses for the x-
height to locate small cap text. We follow this architecture of Tesseract OCR engine to
recognize the Tamil characters.
 To train Tesseract for Tamil script recognition, I followed Complete Training procedure
Specified.
process of Tesseract OCR Engine:

Tamil OCR Component:
 Tesseract OCR Engine trained for tamil language
 Image Extractor
 Image Reader
 Output Manager
 Output formatter
 Text Speaker
Analysis-DFD

 DATA FILES REQUIRED
 To train Tamil Language (lang = tam), I have to created 8 data files in the
tessdata subdirectory. The 8 files are:
1. tessdata/tam.freq-dawg
2. tessdata/tam.word-dawg
3. tessdata/tam.user-words
4. tessdata/tam.inttemp
5. tessdata/tam.normproto
6. tessdata/tam.pffmtable
7. tessdata/tam.unicharset
8. tessdata/tam.DangAmbigs
Analysis- Training Procedure:

Analysis- Web OCR Component
Start
Image Extractor
Tesseract Tamil Trained OCR
Engine
Output Formatter Output Manager
Image Reader
Stop
Webpage
(Web Brower)
Web GUI Page
Tamil Trained
Dataset
Data’s
Fig: Architecture of Web OCR Component

1 Web OCR Component Extract all pictures of web page and save all in
single folder called ‘OCR Image’ (image Extractor)
2. Image Reader will take picture by picture and send to Tesseract
Engine.
3. Tesseract Engine will process and extract text from images only
Tamil and English
4. Output of Tesseract Engine is taken by Output Manger that will
maintain text and images.
5. Output Formatter will put Images and Corresponding Extracted
Text in word document
6 .if User move over any image all process are carried out from 1 to 4
and Text Speaker component will speak out to user.

 Web OCR feature need web browser and it extract all images
from particular page and OCR in the Image. Web OCR has
Following Tools:
 Image Grabber
 Link Grabber
 Web Page to Image Convertor
 Web page to PDF Convertor
 Screen Shotter

 All the above Tools are essential to do Web OCR. Image Grabber
will extract all images and save to Location user Specified. All
extracted images are saved to Folder.

Fig: Web browser

 One of the components of project is Web OCR, as I need Better
GUI it is good to use web browser, because it is software in
which most user and image files are there. This browser module
has low priority in this project. All below mention features are I
mentioned may not be implementing. Simple browser with
required features is enough to developing web browser for
computer using following technologies and concepts are
required:
 Vb.net
 Application programmer interface(API’s)
 Multithreading
 Voice library(voice recognition)

Analysis- Tamil OCR GUI
Start
Input Image
Preprocessing
Tesseract Engine Tamil Trained Dataset
Tamil OCR
Output Text Text
Post Processing
Spell Checker
Tamil Dictionary
Tamil Data files
Accurate Output Text Text
Stop

Input Image
Pre - process
Tamil OCR GUI Output Text
Tesseract OCR Engine Post Process
Tamil and English
Trained Data Set
Output Formats
1. PDF
2. MS Word
3. RTF
4. XML
5. WAV
6. MP3
8. HTML
9. Text
10. Single Web page
Fig: Block Diagram of Full Image OCR

Post Process
Tamil OCR GUI
Selected Image Region
Output Text
Tesseract OCR EnginePre - process
Tamil and English Trained Data Set
Fig: Block Diagram of Region Image OCR

Fig: Block Diagram of Snapshotting OCR

Fig: Block Diagram of Batch OCR

Analysis- Pre-processing
Increase dpi (max 300)
Start
Input Image
Convert to black and white
Preprocess Manager
Remove Background (max 300)
Remove Inner images (max 300)
Preprocessed Image
Fig: Architecture of Pre-processor
Pre-processing is optional process in Tamil OCR.
This is useful to increase Quality of image so that
process of OCR will be more accurate.

The known system is divided into eight functional
modules, which are Independent among them and
they are followed:
 This Project has following modules:
1) Tamil Training to Tesseract
2) Web OCR Component
3) OCR GUI
Design - Modularity

Introduction
The system is designed in two phase:
 Preliminary System Design
 Detailed System Design
 Coupling is the measure of relative interdependence among modules. In Tamil
OCR project Coupling is very loose in nature because we can easily add any
module without problem with any other module or require little modification in
interface calling. Coupling depends of interface complexity between modules,
the point at which entry of reference is made to a module, and what data pass
across the interface.
 Cohesion is the measure of relative functional strength of individual modules. In
 Tamil OCR project Cohesion is very high in nature because performs a single task
within
 a software procedure, requiring little interaction with procedures being
performed in other parts of a program.
Design

1.Training Tesseract-OCR Task
 Tesseract 3.02 is fully trainable. In Tesseract 2.0 it will only accept
Tiff format images, but in the latest version it include leptonica it will
automatically convert any image into tiff format.
 This page describes the training process, provides some guidelines
on applicability to various languages, and what to expect from the
results. Tesseract was originally designed to recognize English text
only. Efforts have been made to modify the engine and its training
system to make them able to deal with other languages and UTF-8
characters.
Design - Modularity

2.Web Tamil OCR-Component Development
1. Web Tamil OCR Component Extract all pictures of web page and save all in
single folder called ‘OCR Image’ (image Extractor)
2. Image Reader will take picture by picture and send to Tesseract Engine.
3. Tesseract Engine will process and extract text from images only Tamil and
English
4. Output of Tesseract Engine is taken by Output Manger that will maintain
text and images.
5. Output Formatter will put Images and Corresponding Extracted Text in
word document
6 .if User move over any image all process are carried out from 1 to 4 and
Text Speaker component will speak out to user.
Design - Modularity

3 .Web browser
 Main component of project is Tamil OCR, as I need Better GUI it is
good to use web browser, because it is software in which most user are
there. This browser module has low priority in this project. Tesseract-OCR is
a command line tool. The Project GUI is going to developed using .net lets
user to easily work with a graphical user interface. Project targeting the
development of component in web browser that component will extract
Tamil character from images.
 The user interface for OCRgui contains the open button to select the image
(tiff format) file, an recognition button that converts the image to an
editable text, a font button that select the font type for the output and
preferences window that have all preferences. Web browser will use Tamil
OCR Component to extract all images text if Tamil and English.
Design - Modularity

Cond….(Web browser )
 The software interfaces used in the project are built VB.NET. It
leads to easily create programs with a graphical user interface
using the Visual basic.net. The Project does not make use of
any particular protocol and therefore there may not be need
for any specified communication interfaces.
Design - Modularity

4 .OCR GUI
OCR GUI allow user to perform various OCR functions more easily
in Tamil. This module provides following features to user.
1. Whole Image OCR
2. Image Region OCR
3. Snap Shot OCR
4. Snipping Screen OCR
5. Batch OCR
6. Various Image Processing functionality
7. Good Tamil Text Editor
8. Easley Tamil Transliteration Typing (like phonetic)
9. Good Tamil Text to Speech
Design - Modularity

 Cont. …..(OCR GUI)
Design - Modularity
10. Image Convertor
11. PDF OCR
12. Various Output Format
13. Various Input Acceptance
14. Easy Typing in Other languages
15. Email Sender
16. Spell checking
17. Snipping and Saving
18. Combined OCR document
19. OCR report

Detailed Design
Fig: Few Class Diagram of OCR GUI

GUI Design
Fig: Whole Page OCR (windows 7 and 8 version)

GUI Design
Fig: Whole Page OCR (windows XP version)

GUI Design
Fig: Snipping OCR Output

GUI Design
Fig: Batch OCR
Windows 8 modes

GUI Design
Fig: Batch OCR
Windows XP mode

GUI Design
Fig: Snapshots OCR in Tamil

GUI Design
Fig: Snapshots OCR in English

GUI Design
Fig: Converted to black and White

GUI Design
Fig: Tamil OCR Help

GUI Design
Fig: Tamil OCR Setting Widow

GUI Design
Fig: Tamil OCR Other Setting
Widow

GUI Design
Fig: Tamil OCR Speak Setting
Widow

Fig: Tamil OCR UI Setting Widow

GUI Design
Fig: Image Preview With image Properties

GUI Design
Fig: Tamil OCR Print Preview
Widow

GUI Design
Fig: Tamil OCR
Batch OCR
Theme Widow

GUI Design
Fig: Tamil OCR with
Blackly Mode

GUI Design
Fig: Web Page Capture

GUI Design
Fig: Web Page Link Grabbing

GUI Design
Fig: Image Grabber
in progress

GUI Design
Fig: Find and High Light Feature

GUI Design
Fig: Snipping and Saving Tool

GUI Design
Fig: Batch OCR Context Help

GUI Design
Fig: Auto Cropped Image

GUI Design
Fig: De Skewed Image

Tesseract-OCR for Tamil language
Increases the number of users of tesseract
GUI makes more people to migrate towards open source
It makes easy for the people to work tesseract, without
the knowledge about it.
Web browser can make use the OCR Tamil component in
the web browser
For blind people it is very good beneficial
Advantages of Project

The applications of Tesseract-OCR are
 News paper industries
 Books publishers.
 Industries of Digital Expertise
 Web browser
 Web Service Creation
 Web Application
 Common People’s
Advantages of Project

 FUTURE ENHANCEMENT
 The accuracy of recognition can be improved by more
training
 AI Applied to make intelligent snapshotting Reader
 Image Content Search
 More Accurate and powerful Spell Checker Corrector
 Powerful web OCR
 Web Service
 Online Web Application
 Handwritten Recognition
 Browser Extensions
 More Powerful Image Preprocessing
Future Enhancement

 I hope the result of the project is that the Tesseract-OCR is
trained for Tamil language and the more training can be used to
train the OCR for tamil language , so accuracy can be increased.
 This Project can increase the number of users to tesseract OCR
among tamil people and most of the Tamil press peoples to
migrate towards free and open source software’s.

The GUI for tesseract will make easy for all the peoples who
don’t have knowledge about tesseract to use it in a perfect
manner.
Conclusion

 The Tamil OCR Project was a great opportunity for me to experience the workings of
software, know about research’s and bring my 2 years of academics to practice. The 4 months
period of Project was very useful for learning the industry specific practices and standards. I am
very confident that this experience will be a great boost and help for my career ahead.
 The period at Dr.K.S kuppusamy was a great learning experience with very helpful project
supervisors. Their evaluation and guiding helped me a lot to learn and understand new techniques
and methodologies. Overall, it was a great experience and will greatly benefit me in future.
 The biggest advantage of Tesseract OCR is its availability as open code. Thus anybody having
the interest to study the working procedure, and skill to improve it can able to train it for a new
language. In this Document, I presented the step by step procedure to train Tesseract engine for
Tamil printed text document. At first we train the Tesseract for a particular font type of English
language that has not been supported earlier by performing a series of test. We then train
Tesseract to recognize the Tamil character set, and observed the results. As we find editing the
box file manually is a cumber-sum task (this language has a large character set), we try to
generate the box file automatically. Also we could be able to detect the vowels and consonants of
Tamil character set, however still we need to train Tesseract for dependent modifiers and other
characters that exist in the Tamil documents, in future.
Conclusion

1. The tesseract-OCR Google groups is developed by “Ray Smith”
and is accessed http://code.google.com/p/tesseract-OCR/
2. Training the tesseract-OCR for Indic languages can be obtained
from http://code.google.com/p/tesseract-
OCR/wiki/TrainingTesseract
3. The tesseract Indic home page is developed and maintained by
“Debayan Banerjee” and can be found
at http://code.google.com/p/tesseractindic/ and etc…
REFERENCES

Tamil OCR using Tesseract OCR Engine

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