최적화 방법을 이용한 문서영상의 텍스트 라인 및 단어 검출법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 조남익.Locating text-lines and segmenting words in a document image are important processes for various document image processing applications such as optical character recognition, document rectification, layout analysis and document image compression. Thus, there have been a lot of researches in this area, and the segmentation of machine-printed documents scanned by flatbed scanners have been matured to some extent. However, in the case of handwritten documents, it is considered a challenging problem since the features of handwritten document are irregular and diverse depending on a person and his/her language. To address this problem, this dissertation presents new segmentation algorithms which extract text-lines and words from a document image based on a new super-pixel representation method and a new energy minimization framework from its characteristics. The overview of the proposed algorithms is as follows. First, this dissertation presents a text-line extraction algorithm for handwritten documents based on an energy minimization framework with a new super-pixel representation scheme. In order to deal with the documents in various languages, a language-independent text-line extraction algorithm is developed based on the super-pixel representation with normalized connected components(CCs). Due to this normalization, the proposed method is able to estimate the states of super-pixels for a range of different languages and writing styles. From the estimated states, an energy function is formulated whose minimization yields text-lines. Experimental results show that the proposed method yields the state-of-the-art performance on various handwritten databases. Second, a preprocessing method of historical documents for text-line detection is presented. Unlike modern handwritten documents, historical documents suffer from various types of degradations. To alleviate these roblems, the preprocessing algorithm including robust binarization and noise removal is introduced in this dissertation. For the robust binarization of historical documents, global and local thresholding binarization methods are combined to deal with various degradations such as stains and fainted characters. Also, the energy minimization framework is modified to fit the characteristics of historical documents. Experimental results on two historical databases show that the proposed preprocessing method with text-line detection algorithm achieves the best detection performance on severely degraded historical documents. Third, this dissertation presents word segmentation algorithm based on structured learning framework. In this dissertation, the word segmentation problem is formulated as a labeling problem that assigns a label (intra- word/inter-word gap) to each gap between the characters in a given text-line. In order to address the feature irregularities especially on handwritten documents, the word segmentation problem is formulated as a binary quadratic assignment problem that considers pairwise correlations between the gaps as well as the likelihoods of individual gaps based on the proposed text-line extraction results. Even though many parameters are involved in the formulation, all parameters are estimated based on the structured SVM framework so that the proposed method works well regardless of writing styles and written languages without user-defined parameters. Experimental results on ICDAR 2009/2013 handwriting segmentation databases show that proposed method achieves the state-of-the-art performance on Latin-based and Indian languages.Abstract i Contents iii List of Figures vii List of Tables xiii 1 Introduction 1 1.1 Text-line Detection of Document Images 2 1.2 Word Segmentation of Document Images 5 1.3 Summary of Contribution 8 2 Related Work 11 2.1 Text-line Detection 11 2.2 Word Segmentation 13 3 Text-line Detection of Handwritten Document Images based on Energy Minimization 15 3.1 Proposed Approach for Text-line Detection 15 3.1.1 State Estimation of a Document Image 16 3.1.2 Problems with Under-segmented Super-pixels for Estimating States 18 3.1.3 A New Super-pixel Representation Method based on CC Partitioning 20 3.1.4 Cost Function for Text-line Segmentation 24 3.1.5 Minimization of Cost Function 27 3.2 Experimental Results of Various Handwritten Databases 30 3.2.1 Evaluation Measure 31 3.2.2 Parameter Selection 31 3.2.3 Experiment on HIT-MW Database 32 3.2.4 Experiment on ICDAR 2009/2013 Handwriting Segmentation Databases 35 3.2.5 Experiment on IAM Handwriting Database 38 3.2.6 Experiment on UMD Handwritten Arabic Database 46 3.2.7 Limitations 48 4 Preprocessing Method of Historical Document for Text-line Detection 53 4.1 Characteristics of Historical Documents 54 4.2 A Combined Approach for the Binarization of Historical Documents 56 4.3 Experimental Results of Text-line Detection for Historical Documents 61 4.3.1 Evaluation Measure and Configurations 61 4.3.2 George Washington Database 63 4.3.3 ICDAR 2015 ANDAR Datasets 65 5 Word Segmentation Method for Handwritten Documents based on Structured Learning 69 5.1 Proposed Approach for Word Segmentation 69 5.1.1 Text-line Segmentation and Super-pixel Representation 70 5.1.2 Proposed Energy Function for Word Segmentation 71 5.2 Structured Learning Framework 72 5.2.1 Feature Vector 72 5.2.2 Parameter Estimation by Structured SVM 75 5.3 Experimental Results 77 6 Conclusions 83 Bibliography 85 Abstract (Korean) 96Docto

A Bottom Up Procedure for Text Line Segmentation of Latin Script

In this paper we present a bottom up procedure for segmentation of text lines written or printed in the Latin script. The proposed method uses a combination of image morphology, feature extraction and Gaussian mixture model to perform this task. The experimental results show the validity of the procedure.Comment: Accepted and presented at the IEEE conference "International Conference on Advances in Computing, Communications and Informatics (ICACCI) 2017

Handwritten Character Recognition of South Indian Scripts: A Review

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure