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 Second, a preprocessing method of historical documents for text-line detection is presented. Unlike modern handwritten documents, historical documents suffer from various types of degradations.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 For the robust binarization of historical documents, global and local thresholding binarization methods are combined to deal with various degradations such as stains and fainted characters. Also, the energy minimization framework is modified to fit the characteristics of historical documents.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

Text-line extraction from handwritten document images using GAN

Text-line extraction (TLE) from unconstrained handwritten document images is still considered an open research problem. Literature survey reveals that use of various rule-based methods is commonplace in this regard. But these methods mostly fail when the document images have touching and/or multi-skewed text lines or overlapping words/characters and non-uniform inter-line space. To encounter this problem, in this paper, we have used a deep learning-based method. In doing so, we have, for the first time in the literature, applied Generative Adversarial Networks (GANs) where we have considered TLE as image-to-image translation task. We have used U-Net architecture for the Generator, and Patch GAN architecture for the discriminator with different combinations of loss functions namely GAN loss, L1 loss and L2 loss. Evaluation is done on two datasets: handwritten Chinese text dataset HIT-MW and ICDAR 2013 Handwritten Segmentation Contest dataset. After exhaustive experimentations, it has been observed that U-Net architecture with combination of the said three losses not only produces impressive results but also outperforms some state-of-the-art methods.Partially supported by the CMATER research laboratory of the Computer Science and Engineering Department, Jadavpur University, India, and the co-author Ram Sarkar is partially funded by DST grant (EMR/2016/007213).http://www.elsevier.com/locate/eswa2021-02-01hj2019Computer Scienc

Neural text line extraction in historical documents: a two-stage clustering approach

Accessibility of the valuable cultural heritage which is hidden in countless scanned historical documents is the motivation for the presented dissertation. The developed (fully automatic) text line extraction methodology combines state-of-the-art machine learning techniques and modern image processing methods. It demonstrates its quality by outperforming several other approaches on a couple of benchmarking datasets. The method is already being used by a wide audience of researchers from different disciplines and thus contributes its (small) part to the aforementioned goal.Das Erschließen des unermesslichen Wissens, welches in unzähligen gescannten historischen Dokumenten verborgen liegt, bildet die Motivation für die vorgelegte Dissertation. Durch das Verknüpfen moderner Verfahren des maschinellen Lernens und der klassischen Bildverarbeitung wird in dieser Arbeit ein vollautomatisches Verfahren zur Extraktion von Textzeilen aus historischen Dokumenten entwickelt. Die Qualität wird auf verschiedensten Datensätzen im Vergleich zu anderen Ansätzen nachgewiesen. Das Verfahren wird bereits durch eine Vielzahl von Forschern verschiedenster Disziplinen genutzt

Recognition of Arabic handwritten words

Recognizing Arabic handwritten words is a difficult problem due to the deformations of different writing styles. Moreover, the cursive nature of the Arabic writing makes correct segmentation of characters an almost impossible task. While there are many sub systems in an Arabic words recognition system, in this work we develop a sub system to recognize Part of Arabic Words (PAW). We try to solve this problem using three different approaches, implicit segmentation and two variants of holistic approach. While Rothacker found similar conclusions while this work is being prepared, we report the difficulty in locating characters in PAW using Scale Invariant Feature Transforms under the first approach. In the second and third approaches, we use holistic approach to recognize PAW using Support Vector Machine (SVM) and Active Shape Models (ASM). While there are few works that use SVM to recognize PAW, they use a small dataset; we use a large dataset and a different set of features. We also explain the errors SVM and ASM make and propose some remedies to these errors as future work

A Multiple-Expert Binarization Framework for Multispectral Images

In this work, a multiple-expert binarization framework for multispectral images is proposed. The framework is based on a constrained subspace selection limited to the spectral bands combined with state-of-the-art gray-level binarization methods. The framework uses a binarization wrapper to enhance the performance of the gray-level binarization. Nonlinear preprocessing of the individual spectral bands is used to enhance the textual information. An evolutionary optimizer is considered to obtain the optimal and some suboptimal 3-band subspaces from which an ensemble of experts is then formed. The framework is applied to a ground truth multispectral dataset with promising results. In addition, a generalization to the cross-validation approach is developed that not only evaluates generalizability of the framework, it also provides a practical instance of the selected experts that could be then applied to unseen inputs despite the small size of the given ground truth dataset.Comment: 12 pages, 8 figures, 6 tables. Presented at ICDAR'1

A Review on Text Detection Techniques

Text detection in image is an important field. Reading text is challenging because of the variations in images. Text detection is useful for many navigational purposes e.g. text on google API’s and traffic panels etc. This paper analyzes the work done on text detection by many researchers and critically evaluates the techniques designed for text detection and states the limitation of each approach. We have integrated the work of many researchers for getting a brief over view of multiple available techniques and their strengths and limitations are also discussed to give readers a clear picture. The major dataset discussed in all these papers are ICDAR 2003, 2005, 2011, 2013 and SVT(street view text).

Image Segmentation and Its Applications Based on the Mumford-Shah Model

Image segmentation is an important topic in computer vision and image processing. As a region-based (global) approach, the Mumford and Shah (MS) model is a powerful and robust segmentation technique as compared to edge-based (local) methods. In this thesis we apply the MS model to two interesting problems: image inpainting and text line detection. We further extend it by proposing a new image segmentation model to overcome some of the difficulties of the original model. As a demonstration of the new model, we apply it to the segmentation of retinal images. The results are better than the state-of-the-art approaches. In image inpainting, the MS model is used to detect and estimate the object boundaries inside the inpainting areas. These boundaries are preserved in the inpainting results. We present a hierarchical segmentation method to detect boundaries of both the main structure and the details. The inpainting result can preserve detailed edges. In text line detection, we use a combination of Gaussian blurring, the MS model, and morphing method. Different from other general text image detection approaches, our method segments text documents without any knowledge of the written texts, so it can detect handwriting text lines of different languages. It can also handle different gaps and overlaps among the text lines. Although the MS model has been used successfully in many applications, its implementation has always been based on some forms of approximation. These approximations are either inefficient computationally or applicable only to some special cases. Our new model consists of only one variable, the segmentation curve, therefore the computation is very efficient. Furthermore, no approximation is required, hence the method can segment objects with complicated intensity distribution. The new model can detect both step and roof edges, and can use different filters to detect objects of different levels of intensity. To show the advantages of the new model, we use a combination of the new model and Gabor filter to detect blood vessels in retinal images. This new model can detect objects with complicated image intensity distribution, and can handle non-uniform illumination cases effectively