optical character recognition

22/04/2023 1

OPTICAL CHARACTER RECOGNITION (OCR)

Introduction

Stages in OCR

MATLAB Implementation

Steps in MATLAB Implementation

Android Implementation

Advantages

Applications

Conclusion

References2

Contents

22/04/2023 3

INTRODUCTION

OCR is the mechanical or electronic translation of images of handwritten, typewritten or printed text (usually captured by a scanner) into machine-editable text.

Motivation:-Text detection and recognition in general have quite a lot of relevant application for automatic indexing or information retrieval such document indexing, content-based image retrieval, and license car plate recognition which further opens up the possibility for more improved and advanced systems.OCR:-

4

Aims and Objectives

Segmentation -Separate the text region into its individual characters.

OCR

Recognition -Recognize each of the character in the detected text region using a suitable algorithm

The goal of Optical Character Recognition (OCR) is to classify optical patterns (often contained in a digital image) corresponding to alphanumeric or other characters.

22/04/2023 5

OCR

TRAINING

Pre - processing

Feature Extraction

Model Estimation

TESTINGPre - processing

Feature Extraction

Classification

STAGES IN OCR

22/04/2023 6

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 :

PRE-PROCESSING

• Binarization• Noise reduction• Stroke width normalization• Skew correction• Slant removal

22/04/2023 7

Binarization (thresholding) refers to the conversion of a gray-scale image into a binary image.

Two categories of thresholding are: 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

BINARIZATION

8

Noise reduction improves the quality of the document.

Normalization provides a tremendous reduction in data size, thinning extracts the shape information of the characters.

Two main approaches:

22/04/2023

Noise Reduction - Normalization

• Filtering (masks)• Morphological Operations (erosion, dilation, etc)

22/04/2023 9

In feature extraction stage each character is represented as a feature vector, which becomes its identity.

The major goal of feature extraction is to extract a set of features, which maximizes the recognition rate with the least amount of elements.

Due to the nature of handwriting with its high degree of variability and imprecision obtaining these features, is a difficult task.

FEATURE EXTRACTION

22/04/2023 10

Given labelled sets of features for many characters, where the labels correspond to the particular classes that the characters belong to, we wish to estimate a statistical model for each character class.

MODEL ESTIMATION

22/04/2023 11

According to Tou and Gonzalez, “The principal function of a pattern recognition system is to yield decisions concerning the class membership of the patterns with which it is confronted.”

In the context of an OCR system, the recognizer is confronted with a sequence feature patterns from which it must determine the character classes.

CLASSIFICATION

12

Flowchart:- MATLAB IMPLEMENTATION

Preprocess

Segmentation

Recognition

22/04/2023 13

Snapshot of MATLAB Application

22/04/2023 14

Make TemplateTo create templete.mat to be use for classification:

14

……36 images of characters

Size = 60 X 55

Matrix size 55 X 60 X 36 Saved as template.mat

22/04/2023 15

PreprocessRaw Image Noise Filter Binarize

ComplimentingBaundingResizing

Preprocessed Image

The segmentation character involves the following steps:

◦ Scan the image from left to right to find ‘on’ pixel.◦ If on pixel been found, all ‘on’ pixel connected to

the detected on pixel will be extracted segmented as a pixel.

◦ The process will be repeated until it reach end right of the image.

16

Segmentation – Connected Components

17

Corr2

Where is the mean of the input matrix i and is the mean of the input matrix j. 0 < r < 1 1 mean i and j is exactly same while 0

mean the i and j not same at all.

18

Source Image Template

image

allcorrs(j) 0.82011 0.57395 0.43850

Recognition - Template Correlations

temp = templates(:,:,j); in = chars(:,:,i); allCorrs(j) = corr2(temp, in);

22/04/2023 19

The same OCR application we build for Android devices named “MyOCR” using open source library “Tesseract” by Google.

Android Implementation

22/04/2023 20

Developed on HP-UX at HP between 1985 and 1994 to run in a desktop scanner. Came neck and neck with Caere and XIS in the 1995 UNLV test. Never used in an HP product. Open sourced in 2005. Now on: http://code.google.com/p/tesseract-ocr Highly portable.

Tesseract Background:-

22/04/2023 21

Tesseract OCR Architecture

22/04/2023 22

ADVANTAGE

Increase efficiency

Greater accessibility

Recover valuable

spaceEliminates

Retyping Need

OCR

22/04/2023 23

APPLICATION

• Page readers for text entry, mainly used in Office Automation

• Aid for blind• Automatic number-plate

readers

• Automatic address reading for mail sorting

• Document reading machines used for Banking Applications

Data entry Process automation

Text EntryOther Applications

22/04/2023 24

Variations in shape• Due to serifs and style variations.

Deformations• Caused by broken characters, smudged characters and

speckle.

Variations in spacing• Due to subscripts, superscripts, skew and variable spacing

Mixture of text and graphics

Typical errors in OCR

22/04/2023 25

Future needs

Need constrained OCR will be decreasing

Omni fontOCR Systems

Recognition of manually produced

documents

Recognition of entire words instead of individual

22/04/2023 26

http://www.uri.edu/~hansenj/projects/ele585/OCR/

J.T. Tou and R.C. Gonzalez, Pattern Recognition Principles, Addison-Wesley Publishing Company, Inc., Reading, Massachusetts, 1974

M. Szmurlo, Masters Thesis, Oslo, May 1995,(users.info.unicaen.fr/~szmurlo/papers/masters/master.thesis.ps.gz)

REFRENCES




22/04/2023 27

THANK YOUSpecial Thanks

To: Google.com Mathwoks.com