The document discusses optical character recognition (OCR) and intelligent character recognition (ICR). It describes the process of OCR/ICR which involves pre-processing scanned documents, segmenting text into characters, extracting features of the characters, and classifying the characters. The pre-processing steps prepare the images for analysis and include binarization, noise removal, and correction of skew and slant. Feature extraction involves statistical techniques like zoning, projections, and distances to represent character images. Classification then identifies the characters using algorithms like k-nearest neighbor or neural networks.

1. Pattern Recognition via Character Recognition

2. Is a system that provides a full alphanumeric recognition of printed or
handwritten characters at electronic speed by simply scanning the form
OCR : Optical Character Recognition
Intelligent Character Recognition (ICR) is used to describe the process of
interpreting image data, in particular alphanumeric text.
Sometimes OCR is known as ICR

3. Functions & Features of OCR
• Forms can be scanned through a scanner and then the recognition
engine of the OCR system interpret the images and turn images of
handwritten or printed characters into ASCII data (machine-readable
characters).
• The technology provides a complete form processing and
documents capture solution.
• Allows an open, scalable and workflow.
• Includes forms definition, scanning, image
• pre-processing, and recognition capabilities.

4. ICR,OCR and OMR Differences
• ICR and OCR are recognition engines used with imaging;
• OMR is a data collection technology that does not require a
recognition engine.
• OMR cannot recognize hand-printed or machine-printed
characters.

5. Optical Mark Reader (OMR)
• Forms
• An OMR works with a specialized document and contains timing
tracks along one edge of the form to indicate scanner where to read
for marks which look like black boxes on the top or bottom of a
form.
• The cut of the form is very precise and the bubbles on a form must
be located in the same location on every form.
• Storage
• With OMR, the image of a document is not scanned and stored.
• Accuracy
• OMR is simpler than OCR.
• designed properly, OMR has more accuracy than OCR.

6. OCR/ ICR
• Forms
• OCR/ ICR is more flexible since no timing tracks or block like form
IDs required.
• The image can float on a page.
• ICR/ OCR technology uses registration mark on the four-corners of a
document, in the recognition of an image. Respondents place one
character per box on this form.
• The use of drop color reduces the size of the scanner’s output and
enhances the accuracy.
• Storage/ retrieval
• If the document needs to be electronically stored and maintained, then
OCR/ ICR is needed.
• OCR/ICR technologies, images can be scanned, indexed, and written to
optical media.

7. OMR-OCR/ICR Compared

8. Advantages and Disadvantages
• Advantages of Using Images Rather Than Paper
• Quicker processing; no moving or storage of questionnaires near
operators
• Savings in costs and efficiencies by not having the paper questionnaires
• Scanning and recognition allowed efficient management and planning for
the rest of the processing workload
• Reduced long term storage requirements, questionnaires could be
destroyed after the initial scanning, recognition and repair
• Quick retrieval for editing and reprocessing
• Minimizes errors associated with physical handling of the questionnaires

9. Disadvantages of Using Images Rather Than Paper
• Accuracy
• While OCR technology can be effective in converting
handwritten or typed characters, it does not give as high
accuracy as of OMR for reading data, where users are
actually marking forms
• Additional workload to data collectors OCR has severe
limitations when it comes to human handwriting
• Characters must be hand-printed with separate
characters in boxes

10. OCR Systems
OCR systems consist of four major stages :
• Pre-processing
• Segmentation
• Feature Extraction
• Classification
• Post-processing

11. Pre-processing
The raw data is subjected to a number of preliminary processing steps to
make it usable in the descriptive stages of character analysis. Pre-
processing aims to produce data that are easy for the OCR systems to
operate accurately. The main objectives of pre-processing are :
• Binarization
• Noise reduction
• Stroke width normalization
• Skew correction
• Slant removal

12. Binarization
Document image binarization (thresholding) refers to the
conversion of a gray-scale image into a binary image. Two
categories of thresholding:
• Global, picks one threshold value for the entire document
image which is often based on an estimation of the
background level from the intensity histogram of the
image.
• Adaptive (local), uses different values for each pixel
according to the local area information

13. Noise Reduction - Normalization
 Normalization provides a tremendous reduction in data size, thinning
extracts the shape information of the characters.
 Noise reduction improves the quality of the document.
Two main approaches:
• Filtering (masks)
• Morphological Operations (erosion, dilation, etc)

14. Skew Correction
 Skew Correction methods are used to align the paper document with the
coordinate system of the scanner. Main approaches for skew detection
include correlation, projection profiles, Hough transform.

15. Slant Removal
 The slant of handwritten texts varies from user to user. Slant
removal methods are used to normalize the all characters to a
standard form.
 Popular deslanting techniques are:
• Calculation of the average angle of near-vertical elements
• Bozinovic – Shrihari Method (BSM).

16. Slant Removal
 Entropy
• The dominant slope of the character is found from the slope corrected
characters which gives the minimum entropy of a vertical projection
histogram. The vertical histogram projection is calculated for a range of
angles ± R. In our case R=60, seems to cover all writing styles. The slope
of the character, ,is found from:
m
a
H
R
a
m


 min
 i
N
i
i p
p
H log
1




• The character is then corrected by using: m
a
)
tan( m
a
y
x
x 

 y
y 


17. Segmentation
 Text Line Detection (Hough Transform, projections,
smearing)
 Word Extraction (vertical projections, connected component
analysis)
 Word Extraction 2 (RLSA)

18. Segmentation
 Explicit Segmentation
In explicit approaches one tries to identify the smallest
possible word segments (primitive segments) that may be
smaller than letters, but surely cannot be segmented further.
Later in the recognition process these primitive segments are
assembled into letters based on input from the character
recognizer. The advantage of the first strategy is that it is
robust and quite straightforward, but is not very flexible.
 Implicit Segmentation
In implicit approaches the words are recognized entirely
without segmenting them into letters. This is most effective
and viable only when the set of possible words is small and
known in advance, such as the recognition of bank checks
and postal address

19. Statistical Features
 Representation of a character image by statistical distribution of
points takes care of style variations to some extent.
 The major statistical features used for character representation are:
• Zoning
• Projections and profiles
• Crossings and distances

20. Zoning
 The character image is divided into NxM zones. From each zone
features are extracted to form the feature vector. The goal of zoning is to
obtain the local characteristics instead of global characteristics

21. Zoning – Density Features
 The number of foreground pixels, or the normalized number of
foreground pixels, in each cell is considered a feature.
Darker squares indicate higher density of zone pixels.

22. Zoning – Direction Features
 Based on the contour of the character image
 For each zone the contour is followed and a directional
histogram is obtained by analyzing the adjacent pixels in a
3x3 neighborhood

23. Zoning – Direction Features
 Based on the skeleton of the character image
 Distinguish individual line segments
 Labeling line segment information
• Line segments are coded with a direction number
2 = vertical line segment
3 = right diagonal line segment
4 = horizontal line segment
5 = left diagonal line segment
 Line type normalization
 Formation of feature vector through zoning
number of
horizontal
lines
total length
of horizontal
lines
number
of
right
diagonal
lines
total length
of right
diagonal
lines
number
of
vertical
lines
total
length of
vertical
lines
number
of
left
diagonal
lines
total length
of left
diagonal
lines
number of
intersection points

24. Projection Histograms
 The basic idea behind using projections is that character images, which are
2-D signals, can be represented as 1-D signal. These features, although
independent to noise and deformation, depend on rotation.
 Projection histograms count the number of pixels in each
column and row of a character image. Projection histograms
can separate characters such as “m” and “n” .

25. Profiles
 The profile counts the number of pixels (distance) between the bounding
box of the character image and the edge of the character. The profiles
describe well the external shapes of characters and allow to distinguish
between a great number of letters, such as “p” and “q”.

26. Profiles
 Profiles can also be used to the contour of the character image
• Extract the contour of the character
• Locate the uppermost and the lowermost points of the
contour
• Calculate the in and out profiles of the contour

27. Crossings and Distances
 Crossings count the number of transitions from background to foreground
pixels along vertical and horizontal lines through the character image and
Distances calculate the distances of the first image pixel detected from the
upper and lower boundaries, of the image, along vertical lines and from the
left and right boundaries along horizontal lines

28. Structural Features
 Characters can be represented by structural features with high
tolerance to distortions and style variations. This type of
representation may also encode some knowledge about the
structure of the object or may provide some knowledge as to what
sort of components make up that object.
 Structural features are based on topological and geometrical
properties of the character, such as aspect ratio, cross points, loops,
branch points, strokes and their directions, inflection between two
points, horizontal curves at top or bottom, etc.

29. Structural Features

30. Structural Features
 A structural feature extraction method for recognizing Greek handwritten
characters [Kavallieratou et.al 2002]
 Three types of features:
• Horizontal and Vertical projection histograms
• Radial histogram
• Radial out-in and radial in-out profiles

31. Global Transformations - Moments
 The Fourier Transform (FT) of the contour of the image is
calculated. Since the first n coefficients of the FT can be used in order
to reconstruct the contour, then these n coefficients are considered to
be a n-dimesional feature vector that represents the character.
 Central, Zenrike moments that make the process of
recognizing an object scale, translation, and rotation invariant.
The original image can be completely reconstructed from the
moment coefficients.

32. Classification
 k-Nearest Neighbour (k-NN) , Bayes Classifier,
Neural Networks (NN), Hidden Markov Models
(HMM), Support Vector Machines (SVM), etc
There is no such thing as the “best classifier”. The use
of classifier depends on many factors, such as
available training set, number of free parameters etc.