Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Teaching Categories to Human Learners with Visual Explanations

We study the problem of computer-assisted teaching with explanations. Conventional approaches for machine teaching typically only provide feedback at the instance level e.g., the category or label of the instance. However, it is intuitive that clear explanations from a knowledgeable teacher can significantly improve a student's ability to learn a new concept. To address these existing limitations, we propose a teaching framework that provides interpretable explanations as feedback and models how the learner incorporates this additional information. In the case of images, we show that we can automatically generate explanations that highlight the parts of the image that are responsible for the class label. Experiments on human learners illustrate that, on average, participants achieve better test set performance on challenging categorization tasks when taught with our interpretable approach compared to existing methods

Rotation-invariant features for multi-oriented text detection in natural images.

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes

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

Template Based Recognition of On-Line Handwriting

Software for recognition of handwriting has been available for several decades now and research on the subject have produced several different strategies for producing competitive recognition accuracies, especially in the case of isolated single characters. The problem of recognizing samples of handwriting with arbitrary connections between constituent characters (emph{unconstrained handwriting}) adds considerable complexity in form of the segmentation problem. In other words a recognition system, not constrained to the isolated single character case, needs to be able to recognize where in the sample one letter ends and another begins. In the research community and probably also in commercial systems the most common technique for recognizing unconstrained handwriting compromise Neural Networks for partial character matching along with Hidden Markov Modeling for combining partial results to string hypothesis. Neural Networks are often favored by the research community since the recognition functions are more or less automatically inferred from a training set of handwritten samples. From a commercial perspective a downside to this property is the lack of control, since there is no explicit information on the types of samples that can be correctly recognized by the system. In a template based system, each style of writing a particular character is explicitly modeled, and thus provides some intuition regarding the types of errors (confusions) that the system is prone to make. Most template based recognition methods today only work for the isolated single character recognition problem and extensions to unconstrained recognition is usually not straightforward. This thesis presents a step-by-step recipe for producing a template based recognition system which extends naturally to unconstrained handwriting recognition through simple graph techniques. A system based on this construction has been implemented and tested for the difficult case of unconstrained online Arabic handwriting recognition with good results

Teaching categories to human learners with visual explanations

We study the problem of computer-assisted teaching with explanations. Conventional approaches for machine teaching typically only provide feedback at the instance level e.g., the category or label of the instance. However, it is intuitive that clear explanations from a knowledgeable teacher can significantly improve a student's ability to learn a new concept. To address these existing limitations, we propose a teaching framework that provides interpretable explanations as feedback and models how the learner incorporates this additional information. In the case of images, we show that we can automatically generate explanations that highlight the parts of the image that are responsible for the class label. Experiments on human learners illustrate that, on average, participants achieve better test set performance on challenging categorization tasks when taught with our interpretable approach compared to existing methods

Integration of traditional imaging, expert systems, and neural network techniques for enhanced recognition of handwritten information

Includes bibliographical references (p. 33-37).Research supported by the I.F.S.R.C. at M.I.T.Amar Gupta, John Riordan, Evelyn Roman

Information Preserving Processing of Noisy Handwritten Document Images

Many pre-processing techniques that normalize artifacts and clean noise induce anomalies due to discretization of the document image. Important information that could be used at later stages may be lost. A proposed composite-model framework takes into account pre-printed information, user-added data, and digitization characteristics. Its benefits are demonstrated by experiments with statistically significant results. Separating pre-printed ruling lines from user-added handwriting shows how ruling lines impact people\u27s handwriting and how they can be exploited for identifying writers. Ruling line detection based on multi-line linear regression reduces the mean error of counting them from 0.10 to 0.03, 6.70 to 0.06, and 0.13 to 0.02, com- pared to an HMM-based approach on three standard test datasets, thereby reducing human correction time by 50%, 83%, and 72% on average. On 61 page images from 16 rule-form templates, the precision and recall of form cell recognition are increased by 2.7% and 3.7%, compared to a cross-matrix approach. Compensating for and exploiting ruling lines during feature extraction rather than pre-processing raises the writer identification accuracy from 61.2% to 67.7% on a 61-writer noisy Arabic dataset. Similarly, counteracting page-wise skew by subtracting it or transforming contours in a continuous coordinate system during feature extraction improves the writer identification accuracy. An implementation study of contour-hinge features reveals that utilizing the full probabilistic probability distribution function matrix improves the writer identification accuracy from 74.9% to 79.5%