Rami Al-Sahhar

Ideas for today and tomorrow

Agenda OCR Overview

The Arabic OCR Problem

OCR Challenges

Proposed Solution Detailed system stages

Sample Run

Future Work

Demo

OCR Overview (OCR) is the process of converting an image of text, such as

a scanned paper document, into computer-editable text

The ultimate goal of OCR is to simulate the human ability to read both machine-printed and hand-written texts

Most of the work on OCR has been on Latin and Chinese characters

Arabic character recognition started recently and advanced relatively slowly due to the complexity of recognizing Arabic text, which has characters that are cursive in nature.

Arabic character recognition is still an open and challenging field of research

The Arabic OCR Problem

To propose a complete system that classifies and recognizes machine-printed Arabic text

The input to the system is TIFF image file

The Arabic font size varies from 8 up to 36

The font type is Arabic Simplified or Traditional Arabic

The image scanned at 300 dpi ( Resolution )

The output is editable text in a word processor program ( MS Word)

OCR Challenges

Understanding TIFF image format and pixel representation

Programmatically , read TIFF image pixel by pixel from right to left

Features extraction

Segmentation free

Spaces ,Words , Letters and Line isolation

Noise reduction

Dots and holes

Overlapped characters

OCR Challenges Arabic Character Characteristics

Right to left

Always cursive

Change of character shape according to its location in the word

Four different shapes

28 basic characters: 15 with dots, 13 without

No fixed character width and no fixed size

OCR Challenges Arabic Character Characteristics

A sample of written Arabic showing some of its characteristics

Group of Arabic character shapes

The Proposed Solution

The proposed system starts from the document image acquisition stage and ends with recognized Arabic text in standard Simplified true type font format in MS Word 2007

We started designing our system by experimenting with prior researchers’ techniques, adopting or modifying some of them if they met our requirements, but otherwise developing our own techniques

Consequently, the components of our system are either due to the work of others, the result of our improvement of others’ work, or our own completely new techniques.

The Proposed Solution

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TIFFIMAGE FILE

PREPROCESSING

FEATURE EXTRACTION

POSTENHANCEMENT

CLASSIFICATION AND

RECOGNITION

RECOGNIZED TEXT

ATR (Arabic Text Recognition)

System model

The Proposed Solution

Preprocessing PhaseDigitalization, scaling, word-level segmentation, noise removal and elimination of redundant information as far as possible Image information retrieval

Load/Read the input (TIFF) image file as binary; retrieve the image properties (size, width, height, pixel resolution, image channels and image alignment; and create memory storage for system intermediate processing

Image digitalization Digitizes the TIFF image in order to apply fixed-level

thresholding and to convert the gray-scale and bitmapped image to a binary (0’s and 1’s representation) scale image

The Proposed Solution

It does the vertical and horizontal histograms to retrieve the number of lines per page and number of components (words) per each line

We calculate the font baseline and size by finding the maximum horizontal histogram of each line per page

This enables the dots or other special characters such as Shadda, Madda, and Tanween to be classified as upper or lower components related to this baseline Text line detection Word segmentation

The Proposed SolutionB&W image is found in file name: [ test1.tif]

Processing a [1615x2160] image with [1] channel(s)

Image Origin : [Top-left Origin] , Align : [4-]

Data Order :[Interleaved Color Channels]

Number of Lines(s) found: [6]

Line #0 , Y = 78 , Height = [67]

Line #1 , Y = 185 , Height =[ 67]

Line #2 , Y = 292 , Height = [67]

Line #3 , Y = 399 , Height = [67]

Line #4 , Y = 506 , Height = [67]

Line #5 , Y = 613 , Height = [67]

Font Baseline =[ 38 pixels]

Number of Components found at Image Line #0 : [9]

Number of Components found at Image Line #1 : [14]

Number of Components found at Image Line #2 : [16]

Number of Components found at Image Line #3 : [18]

Number of Components found at Image Line #4 : [10]

Number of Components found at Image Line #5 : [6]

B&W image is found in file name: [ test1.tif]

Processing a [1615x2160] image with [1] channel(s)

Image Origin : [Top-left Origin] , Align : [4-]

Data Order :[Interleaved Color Channels]

Number of Lines(s) found: [6]

Line #0 , Y = 78 , Height = [67]

Line #1 , Y = 185 , Height =[ 67]

Line #2 , Y = 292 , Height = [67]

Line #3 , Y = 399 , Height = [67]

Line #4 , Y = 506 , Height = [67]

Line #5 , Y = 613 , Height = [67]

Font Baseline =[ 38 pixels]

Number of Components found at Image Line #0 : [9]

Number of Components found at Image Line #1 : [14]

Number of Components found at Image Line #2 : [16]

Number of Components found at Image Line #3 : [18]

Number of Components found at Image Line #4 : [10]

Number of Components found at Image Line #5 : [6]

Preprocessing phase Text Line Detection

The Proposed Solution

Number of retrieved contours : [2]

************* Bounding Rectangle (1,22)-(72,65) ************

Component [1] Origin Y = 22 , Height = 43 Area = 429.000000

Component [2] Origin Y = 47, Height = 7 Area = 19.000000

Max Component Area = 429.000000 , Y = 22 , H = 43

Number of retrieved contours : [2]

************* Bounding Rectangle (1,22)-(72,65) ************

Component [1] Origin Y = 22 , Height = 43 Area = 429.000000

Component [2] Origin Y = 47, Height = 7 Area = 19.000000

Max Component Area = 429.000000 , Y = 22 , H = 43

21 Preprocessing phase Word

Segmentation

The Proposed Solution Feature Extraction Phase

Is the most challenging part for character or text recognition The choice of good features significantly improves the

recognition rate and minimizes the error in case of noise The main selected features are :

Outer contours described in Freeman chain codes Contours’ corners Dot information Font estimated size

All of these features are extracted for all detected components during the page scanning

The Proposed Solution Freeman chain code

Chain code was introduced by Freeman as a mean of representing lines or boundaries of shapes by a connected sequence of straight-line segments of specified length and direction

An example of the 8-connectivity chain codeChain code numbering schemes

The Proposed Solution Contour extraction process

This is the core process to extract the main word-level features of the Arabic text in Freeman Chain code format

After extracting the Freeman codes, we aggregate those codes into pairs as (X, Y) where X is the direction (i.e. from 1 to 7) and Y is the length in pixels

The Proposed SolutionContours Freeman Chain Codes

[2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,2,3,2,3,6,5,6,6,5,7,7,7,6,7,6,6,6,6,5,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,4,3,1,1,1,1,2,2,2,3,4,4,3,4,4,4,4,4,4,4,4,5,4,6,5,6,6,6,1,0,0,7,7,7,5,4,5,4,4,5,4,4,4,4,4,3,2,2,2,2,2,2,2,2,2,3,2,3,2,3,6,5,6,5,7,6,7,7,6,7,6,6,6,6,5,4,4,4,4,4,4,4,4,4,4,4,4,4,3,2,2,2,2,2,2,2,3,2,2,2,3,2,3,2,3,3,3,3,4,3,6,6,6,6,7,0,7,7,7,7,6,7,6,6,6,7,6,6,6,6,6,5,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,3,6,6,6,6,7,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,1,7,7,7,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]

Contours Freeman Chain Code Pairs - Aligned

Total Pairs : [95] ==> [(2,14),(3,1),(2,1),(3,1),(2,1),(3,1),(6,1),(5,1),(6,2),(5,1),(7,3),(6,1),(7,1),(6,4),(5,1),(4,19),(3,1),(4,1),(3,1),(1,4),(2,3),(3,1),(4,2),(3,1),(4,8),(5,1),(4,1),(6,1),(5,1),(6,3),(1,1),(0,2),(7,3),(5,1),(4,1),(5,1),(4,2),(5,1),(4,5),(3,1),(2,9),(3,1),(2,1),(3,1),(2,1),(3,1),(6,1),(5,1),(6,1),(5,1),(7,1),(6,1),(7,2),(6,1),(7,1),(6,4),(5,1),(4,13),(3,1),(2,7),(3,1),(2,3),(3,1),(2,1),(3,1),(2,1),(3,4),(4,1),(3,1),(6,4),(7,1),(0,1),(7,4),(6,1),(7,1),(6,3),(7,1),(6,5),(5,1),(4,14),(3,2),(6,4),(7,2),(0,38),(1,1),(0,2),(1,1),(0,1),(1,1),(0,1),(1,1),(7,3),(0,1),(7,1),(0,22)]

Contour Corners Positions:

[5,18,23,33,37,60,67,81,87,90,92,103,113,118,132,146,160,162,168,172,178,180,189,202,206,210,258,263,]

Contours Freeman Chain Codes

[2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,2,3,2,3,6,5,6,6,5,7,7,7,6,7,6,6,6,6,5,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,4,3,1,1,1,1,2,2,2,3,4,4,3,4,4,4,4,4,4,4,4,5,4,6,5,6,6,6,1,0,0,7,7,7,5,4,5,4,4,5,4,4,4,4,4,3,2,2,2,2,2,2,2,2,2,3,2,3,2,3,6,5,6,5,7,6,7,7,6,7,6,6,6,6,5,4,4,4,4,4,4,4,4,4,4,4,4,4,3,2,2,2,2,2,2,2,3,2,2,2,3,2,3,2,3,3,3,3,4,3,6,6,6,6,7,0,7,7,7,7,6,7,6,6,6,7,6,6,6,6,6,5,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,3,6,6,6,6,7,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,1,7,7,7,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]

Contours Freeman Chain Code Pairs - Aligned

Total Pairs : [95] ==> [(2,14),(3,1),(2,1),(3,1),(2,1),(3,1),(6,1),(5,1),(6,2),(5,1),(7,3),(6,1),(7,1),(6,4),(5,1),(4,19),(3,1),(4,1),(3,1),(1,4),(2,3),(3,1),(4,2),(3,1),(4,8),(5,1),(4,1),(6,1),(5,1),(6,3),(1,1),(0,2),(7,3),(5,1),(4,1),(5,1),(4,2),(5,1),(4,5),(3,1),(2,9),(3,1),(2,1),(3,1),(2,1),(3,1),(6,1),(5,1),(6,1),(5,1),(7,1),(6,1),(7,2),(6,1),(7,1),(6,4),(5,1),(4,13),(3,1),(2,7),(3,1),(2,3),(3,1),(2,1),(3,1),(2,1),(3,4),(4,1),(3,1),(6,4),(7,1),(0,1),(7,4),(6,1),(7,1),(6,3),(7,1),(6,5),(5,1),(4,14),(3,2),(6,4),(7,2),(0,38),(1,1),(0,2),(1,1),(0,1),(1,1),(0,1),(1,1),(7,3),(0,1),(7,1),(0,22)]

Contour Corners Positions:

[5,18,23,33,37,60,67,81,87,90,92,103,113,118,132,146,160,162,168,172,178,180,189,202,206,210,258,263,]

Feature extraction: Freeman chain codes, pairs and corner positions

The Proposed Solution Corner Detection

This phase detects and extracts the component’s contour corners of the text under processing

It is based on an implementation of contour detection and curve representation by circular local histogram of contour chain code presented by [Arrebola, Camacho, Bandera , & Sandoval

(1999)]

The corner detection phase is very important for the next classification and recognition phase

It helps our Prolog engine to determine the unique shape of the character’s feature regardless of the character orientation

The output of this phase is a stream of corner information to be input for the next phase

The Proposed Solution Contour

enhancement We introduced an algorithm to remove noisy pixels which come within any straight line, and to convert Arabic characters to approximately straight lines.

These enhancement rules, which are derived from testing Arabic characters multiple times, reduce the time required for character recognition

The Proposed Solution

Dot Detection and Font Size Estimation

Dot detection is another challenging important task It helps the prolog classification engine to recognize words that

include dots and it decides which characters are un-dotted by nature

Font size estimation is an critical task in Omni-size character recognition systems

Font size estimation is usually used to find the pen width in online recognition systems

Approaches used to estimate the font size by dots is presented in [Shirali-Shahreza , & Shirali-Shahreza (2006)]

The Proposed Solution

The Proposed Solution

Font size against calculated component’s height (in pixels)

The Proposed Solution

Definite Clause Grammar (DCG): Provides a mechanism for defining the grammar rules of a

language These rules are automatically translated to a Prolog program

which defines a parser for the language being defined Grammar rules are a feature only in some Prolog systems,

and are designed to facilitate the parsing of natural language Using this notation, a grammar is represented as a set of

logical rules When the DCG rules are consulted (or optimized), they are

translated into Prolog clauses

The Proposed Solution

Word-level Classification and Recognition Phase This is the most critical phase in our proposed ATR system It is written in Prolog language using Prolog matching,

backtracking and DCG techniques The input for this phase is data on two features : The first input stream is the corner sequence of the word-level

outer contours for each component that represents the elevation information of the input stream (the upper part that holds most of the features)

The second input stream is the dot information found in the same component

The Proposed Solution

The Prolog matching and backtracking techniques also use the corner sequence stream to classify the unknown inputs into character classes, while the Prolog DCG technique uses the dot information stream to recognize the actual Arabic letters of a particular character class

The Proposed Solution

% DCG part for Arabic text recognition based on two input streams

% usage: phrase(s(R),[m,h_c,d1,m,dc]).

s([H|T]) -->cc(H), subs(T). % every string is a character class followed by a sub-string

s(R)-->cc(R). % or a string can be simply a character class

subs(R)-->s(R). % a substring is nothing but a string (recursively)

cc(R)-->ch(R). % a character class can be a simple character

% or character classes can belong to any of the following classes

cc(R)-->bc(R). % Ba class (ba, ta, tha, ya_md)

cc(R)-->h_c(R). % H_ class (h_, jeem, kha)

cc(R)-->dc(R). % Dal Class (dal, thal)

cc(R)-->rc(R). % Ra' Class (ra, zay)

cc(R)-->sc(R). % Seen Class (seen, sheen)

DCG implementation: The DCG grammar structure and some of the character classes are described below :

The Proposed Solution Microsoft Word Document Integration

This is the final phase of our optical text recognition system It is written in Prolog language to interface with Microsoft Word

program It uses Microsoft Word Document API to write the recognized

characters into a new Word document It writes the output text in the same recognized font size in a

predefined font type It also writes the white spaces and new lines to maintain the

same original text alignment and format

The Proposed Solution

Character Reference Database

Character Reference Database

Freeman Chain CodesFreeman Chain Codes

Lines per PageLines per PageWords per LineWords per Line

Pixel ResolutionPixel Resolution

Recognized Text (Word Document)

Recognized Text (Word Document)

DCG EngineDCG Engine

Document Image (TIFF File)

Document Image (TIFF File)

Component Area, Height and Width

Component Area, Height and Width

Component Coordinates(X, Y)

Component Coordinates(X, Y)

Word-levelSegmentationWord-level

Segmentation

ImageDigitalization

ImageDigitalization

Word-levelContour Extraction

Word-levelContour Extraction

Image Information Retrieval

Image Information Retrieval

Corner DetectionCorner Detection

Dots DetectionDots Detection

Character-ShapeProlog Matching

Character-ShapeProlog Matching

Word-levelRecognition

Word-levelRecognition

MS WORD Document Integration

MS WORD Document Integration

Font SizeFont Size

Height/ WidthHeight/ Width

Font BaselineFont Baseline

Contour EnhancementContour Enhancement

Dot Information Stream

Character Shape Stream

Sample RunOriginal TIFF image with Arabic text The recognized Arabic text in MS

Word 2007

Future Work

Support more Arabic font types

Support more image types ( GIF , BMP , JPEG…etc)

Support different font sizes in same page

Support Arabic & English fonts together , numeric and special characters

Support Spellchecker and word suggestions

Implement the system as Arabic Business Card reader

Capture and Recognize feature for iPhone

Demo

OCR Applications

Industries and Institutions in which control of large amounts of paper work is critical Banking, Credit cards, Insurance industries

The medical community To capture, store and transmit radiology images

Libraries and archives For conservation and preservation of vulnerable documents and

for the provision of access to source documents

Thank You

rami.sahhar@gmail.com