Segmentation of touching characters in printed Devnagari and Bangla scripts using fuzzy multifactorial analysis
01 Nov 2002-Vol. 32, Iss: 4, pp 449-459
TL;DR: A new technique is presented for identification and segmentation of touching characters based on fuzzy multifactorial analysis and a predictive algorithm is developed for effectively selecting possible cut columns for segmenting the touching characters.
Abstract: One of the important reasons for poor recognition rate in optical character recognition (OCR) system is the error in character segmentation. Existence of touching characters in the scanned documents is a major problem to design an effective character segmentation procedure. In this paper, a new technique is presented for identification and segmentation of touching characters. The technique is based on fuzzy multifactorial analysis. A predictive algorithm is developed for effectively selecting possible cut columns for segmenting the touching characters. The proposed method has been applied to printed documents in Devnagari and Bangla: the two most popular scripts of the Indian sub-continent. The results obtained from a test-set of considerable size show that a reasonable improvement in recognition rate can be achieved with a modest increase in computations.
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TL;DR: A review of the OCR work done on Indian language scripts and the scope of future work and further steps needed for Indian script OCR development is presented.
Abstract: Intensive research has been done on optical character recognition (OCR) and a large number of articles have been published on this topic during the last few decades. Many commercial OCR systems are now available in the market. But most of these systems work for Roman, Chinese, Japanese and Arabic characters. There are no sufficient number of work on Indian language character recognition although there are 12 major scripts in India. In this paper, we present a review of the OCR work done on Indian language scripts. The review is organized into 5 sections. Sections 1 and 2 cover introduction and properties on Indian scripts. In Section 3, we discuss different methodologies in OCR development as well as research work done on Indian scripts recognition. In Section 4, we discuss the scope of future work and further steps needed for Indian script OCR development. In Section 5 we conclude the paper.
592 citations
TL;DR: A neural network is proposed for Gujarati handwritten digits identification and a multi layered feed forward Neural network is suggested for classification of digits.
Abstract: This paper deals with an optical character recognition (OCR) system for handwritten Gujarati numbers. One may find so much of work for Indian languages like Hindi, Kannada, Tamil, Bangala, Malayalam, Gurumukhi etc, but Gujarati is a language for which hardly any work is traceable especially for handwritten characters. Here in this work a neural network is proposed for Gujarati handwritten digits identification. A multi layered feed forward neural network is suggested for classification of digits. The features of Gujarati digits are abstracted by four different profiles of digits. Thinning and skew-correction are also done for preprocessing of handwritten numerals before their classification. This work has achieved approximately 82% of success rate for Gujarati handwritten digit identification.
176 citations
Cites methods from "Segmentation of touching characters..."
...[8] proposed an algorithm, based on fuzzy multi-factorial analysis for segmentation of touching printed Devnagari and Bangala scripts....
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01 Nov 2011
TL;DR: In this paper, the state of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in various sections of the paper.
Abstract: In India, more than 300 million people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. State of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in this paper. All feature-extraction techniques as well as training, classification and matching techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date. Moreover, the paper also contains a comprehensive bibliography of many selected papers appeared in reputed journals and conference proceedings as an aid for the researchers working in the field of Devanagari OCR.
159 citations
TL;DR: A review of OCR work on Indian scripts, mainly on Bangla and Devanagari—the two most popular scripts in India, and the various methodologies and their reported results are presented.
Abstract: The past few decades have witnessed an intensive research on optical character recognition (OCR) for Roman, Chinese, and Japanese scripts. A lot of work has been also reported on OCR efforts for various Indian scripts, like Devanagari, Bangla, Oriya, Tamil, Telugu, Malayalam, Kannada, Gurmukhi, Gujarati, etc. In this paper, we present a review of OCR work on Indian scripts, mainly on Bangla and Devanagari—the two most popular scripts in India. We have summarized most of the published papers on this topic and have also analysed the various methodologies and their reported results. Future directions of research in OCR for Indian scripts have been also given.
70 citations
TL;DR: This paper is the first survey that focuses on touched character segmentation and provides segmentation rates, descriptions of the test data for the approaches discussed, and the main trends in the field of touched character segmentsation.
Abstract: Character segmentation is a challenging problem in the field of optical character recognition. Presence of touched characters make this dilemma more crucial. The goal of this paper is to provide major concepts and progress in domain of off-line cursive touched character segmentation. Accordingly, two broad classes of technique are identified. These include methods that perform explicit or implicit character segmentation. The basic methods used by each class of technique are presented and the contributions of individual algorithms within each class are discussed. It is the first survey that focuses on touched character segmentation and provides segmentation rates, descriptions of the test data for the approaches discussed. Finally, the main trends in the field of touched character segmentation are examined, important contributions are presented and future directions are also suggested.
69 citations
Cites methods from "Segmentation of touching characters..."
...Comprehensive observation related to touch Bangla character problem based on statistical features is reported in Garain and Chaudhuri (2002)....
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...For segmentation based approach, the basic principle to solve touching characters is by producing the segmentation hypothesis using some criteria/function (Garain and Chaudhuri 2002)....
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References
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Book•
01 Jan 1986TL;DR: This book discusses the design of a Code Generator, the role of the Lexical Analyzer, and other topics related to code generation and optimization.
Abstract: 1 Introduction 1.1 Language Processors 1.2 The Structure of a Compiler 1.3 The Evolution of Programming Languages 1.4 The Science of Building a Compiler 1.5 Applications of Compiler Technology 1.6 Programming Language Basics 1.7 Summary of Chapter 1 1.8 References for Chapter 1 2 A Simple Syntax-Directed Translator 2.1 Introduction 2.2 Syntax Definition 2.3 Syntax-Directed Translation 2.4 Parsing 2.5 A Translator for Simple Expressions 2.6 Lexical Analysis 2.7 Symbol Tables 2.8 Intermediate Code Generation 2.9 Summary of Chapter 2 3 Lexical Analysis 3.1 The Role of the Lexical Analyzer 3.2 Input Buffering 3.3 Specification of Tokens 3.4 Recognition of Tokens 3.5 The Lexical-Analyzer Generator Lex 3.6 Finite Automata 3.7 From Regular Expressions to Automata 3.8 Design of a Lexical-Analyzer Generator 3.9 Optimization of DFA-Based Pattern Matchers 3.10 Summary of Chapter 3 3.11 References for Chapter 3 4 Syntax Analysis 4.1 Introduction 4.2 Context-Free Grammars 4.3 Writing a Grammar 4.4 Top-Down Parsing 4.5 Bottom-Up Parsing 4.6 Introduction to LR Parsing: Simple LR 4.7 More Powerful LR Parsers 4.8 Using Ambiguous Grammars 4.9 Parser Generators 4.10 Summary of Chapter 4 4.11 References for Chapter 4 5 Syntax-Directed Translation 5.1 Syntax-Directed Definitions 5.2 Evaluation Orders for SDD's 5.3 Applications of Syntax-Directed Translation 5.4 Syntax-Directed Translation Schemes 5.5 Implementing L-Attributed SDD's 5.6 Summary of Chapter 5 5.7 References for Chapter 5 6 Intermediate-Code Generation 6.1 Variants of Syntax Trees 6.2 Three-Address Code 6.3 Types and Declarations 6.4 Translation of Expressions 6.5 Type Checking 6.6 Control Flow 6.7 Backpatching 6.8 Switch-Statements 6.9 Intermediate Code for Procedures 6.10 Summary of Chapter 6 6.11 References for Chapter 6 7 Run-Time Environments 7.1 Storage Organization 7.2 Stack Allocation of Space 7.3 Access to Nonlocal Data on the Stack 7.4 Heap Management 7.5 Introduction to Garbage Collection 7.6 Introduction to Trace-Based Collection 7.7 Short-Pause Garbage Collection 7.8 Advanced Topics in Garbage Collection 7.9 Summary of Chapter 7 7.10 References for Chapter 7 8 Code Generation 8.1 Issues in the Design of a Code Generator 8.2 The Target Language 8.3 Addresses in the Target Code 8.4 Basic Blocks and Flow Graphs 8.5 Optimization of Basic Blocks 8.6 A Simple Code Generator 8.7 Peephole Optimization 8.8 Register Allocation and Assignment 8.9 Instruction Selection by Tree Rewriting 8.10 Optimal Code Generation for Expressions 8.11 Dynamic Programming Code-Generation 8.12 Summary of Chapter 8 8.13 References for Chapter 8 9 Machine-Independent Optimizations 9.1 The Principal Sources of Optimization 9.2 Introduction to Data-Flow Analysis 9.3 Foundations of Data-Flow Analysis 9.4 Constant Propagation 9.5 Partial-Redundancy Elimination 9.6 Loops in Flow Graphs 9.7 Region-Based Analysis 9.8 Symbolic Analysis 9.9 Summary of Chapter 9 9.10 References for Chapter 9 10 Instruction-Level Parallelism 10.1 Processor Architectures 10.2 Code-Scheduling Constraints 10.3 Basic-Block Scheduling 10.4 Global Code Scheduling 10.5 Software Pipelining 10.6 Summary of Chapter 10 10.7 References for Chapter 10 11 Optimizing for Parallelism and Locality 11.1 Basic Concepts 11.2 Matrix Multiply: An In-Depth Example 11.3 Iteration Spaces 11.4 Affine Array Indexes 11.5 Data Reuse 11.6 Array Data-Dependence Analysis 11.7 Finding Synchronization-Free Parallelism 11.8 Synchronization Between Parallel Loops 11.9 Pipelining 11.10 Locality Optimizations 11.11 Other Uses of Affine Transforms 11.12 Summary of Chapter 11 11.13 References for Chapter 11 12 Interprocedural Analysis 12.1 Basic Concepts 12.2 Why Interprocedural Analysis? 12.3 A Logical Representation of Data Flow 12.4 A Simple Pointer-Analysis Algorithm 12.5 Context-Insensitive Interprocedural Analysis 12.6 Context-Sensitive Pointer Analysis 12.7 Datalog Implementation by BDD's 12.8 Summary of Chapter 12 12.9 References for Chapter 12 A A Complete Front End A.1 The Source Language A.2 Main A.3 Lexical Analyzer A.4 Symbol Tables and Types A.5 Intermediate Code for Expressions A.6 Jumping Code for Boolean Expressions A.7 Intermediate Code for Statements A.8 Parser A.9 Creating the Front End B Finding Linearly Independent Solutions Index
8,437 citations
"Segmentation of touching characters..." refers methods in this paper
...In our approach, we have attempted to reduce the computation by predicting the most favorable cut column before character classifier is used to confirm it. This concept is borrowed from the predictive parser concept that is well known in the field of compiler design for the programming languages [ 19 ]....
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...For this purpose, we use a concept similar to the concepts of predictive parser [ 19 ] used in compiler design for programming languages....
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TL;DR: H holistic approaches that avoid segmentation by recognizing entire character strings as units are described, including methods that partition the input image into subimages, which are then classified.
Abstract: Character segmentation has long been a critical area of the OCR process. The higher recognition rates for isolated characters vs. those obtained for words and connected character strings well illustrate this fact. A good part of recent progress in reading unconstrained printed and written text may be ascribed to more insightful handling of segmentation. This paper provides a review of these advances. The aim is to provide an appreciation for the range of techniques that have been developed, rather than to simply list sources. Segmentation methods are listed under four main headings. What may be termed the "classical" approach consists of methods that partition the input image into subimages, which are then classified. The operation of attempting to decompose the image into classifiable units is called "dissection." The second class of methods avoids dissection, and segments the image either explicitly, by classification of prespecified windows, or implicitly by classification of subsets of spatial features collected from the image as a whole. The third strategy is a hybrid of the first two, employing dissection together with recombination rules to define potential segments, but using classification to select from the range of admissible segmentation possibilities offered by these subimages. Finally, holistic approaches that avoid segmentation by recognizing entire character strings as units are described.
880 citations
"Segmentation of touching characters..." refers background in this paper
...Fujisawa et al. [3], Elliman and Lancaster [6], Casey and Lecolinet [ 7 ], have presented elaborate surveys on character segmentation....
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TL;DR: The document spectrum (or docstrum) as discussed by the authors is a method for structural page layout analysis based on bottom-up, nearest-neighbor clustering of page components, which yields an accurate measure of skew, within-line, and between-line spacings and locates text lines and text blocks.
Abstract: Page layout analysis is a document processing technique used to determine the format of a page. This paper describes the document spectrum (or docstrum), which is a method for structural page layout analysis based on bottom-up, nearest-neighbor clustering of page components. The method yields an accurate measure of skew, within-line, and between-line spacings and locates text lines and text blocks. It is advantageous over many other methods in three main ways: independence from skew angle, independence from different text spacings, and the ability to process local regions of different text orientations within the same image. Results of the method shown for several different page formats and for randomly oriented subpages on the same image illustrate the versatility of the method. We also discuss the differences, advantages, and disadvantages of the docstrum with respect to other lay-out methods. >
654 citations
Book•
01 Jan 1995
TL;DR: The document spectrum (or docstrum), which is a method for structural page layout analysis based on bottom-up, nearest-neighbor clustering of page components, yields an accurate measure of skew, within-line, and between-line spacings and locates text lines and text blocks.
Abstract: Page layout analysis is a document processing technique used to determine the format of a page. This paper describes the document spectrum (or docstrum), which is a method for structural page layout analysis based on bottom-up, nearest-neighbor clustering of page components. The method yields an accurate measure of skew, within-line, and between-line spacings and locates text lines and text blocks. It is advantageous over many other methods in three main ways: independence from skew angle, independence from different text spacings, and the ability to process local regions of different text orientations within the same image. Results of the method shown for several different page formats and for randomly oriented subpages on the same image illustrate the versatility of the method. We also discuss the differences, advantages, and disadvantages of the docstrum with respect to other lay-out methods. >
628 citations
"Segmentation of touching characters..." refers methods in this paper
...There exists a wide variety of skew detection algorithms based on projection profile, Hough transform, nearest neighbor clustering [20], docstrum analysis [ 21 ], line correlation [22], etc....
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TL;DR: A complete Optical Character Recognition (OCR) system for printed Bangla, the fourth most popular script in the world, is presented and extension of the work to Devnagari, the third most popular Script in the World, is discussed.
Abstract: A complete Optical Character Recognition (OCR) system for printed Bangla, the fourth most popular script in the world, is presented This is the first OCR system among all script forms used in the Indian sub-continent The problem is difficult because (i) there are about 300 basic, modified and compound character shapes in the script, (ii) the characters in a word are topologically connected and (iii) Bangla is an inflectional language In our system the document image captured by Flat-bed scanner is subject to skew correction, text graphics separation, line segmentation, zone detection, word and character segmentation using some conventional and some newly developed techniques From zonal information and shape characteristics, the basic, modified and compound characters are separated for the convenience of classification The basic and modified characters which are about 75 in number and which occupy about 96% of the text corpus, are recognized by a structural-feature-based tree classifier The compound characters are recognized by a tree classifier followed by template-matching approach The feature detection is simple and robust where preprocessing like thinning and pruning are avoided The character unigram statistics is used to make the tree classifier efficient Several heuristics are also used to speed up the template matching approach A dictionary-based error-correction scheme has been used where separate dictionaries are compiled for root word and suffixes that contain morpho-syntactic informations as well For single font clear documents 9550% word level (which is equivalent to 9910% character level) recognition accuracy has been obtained Extension of the work to Devnagari, the third most popular script in the world, is also discussed
381 citations
"Segmentation of touching characters..." refers background in this paper
...On the other hand, development of OCR systems for the Indian language scripts are gaining importance because of their large market potential (more than 500 million people in India, Bangladesh and Nepal use Devnagari and Bangla scripts to write their language) and as a result, a Bangla OCR system [4] and later on, a bilingual (Devnagari & Bangla) OCR system [5] is developed....
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