Document analysis at DFKI. - Part 1: Image analysis and text recognition

Document analysis is responsible for an essential progress in office automation. This paper is part of an overview about the combined research efforts in document analysis at the DFKI. Common to all document analysis projects is the global goal of providing a high level electronic representation of documents in terms of iconic, structural, textual, and semantic information. These symbolic document descriptions enable an "intelligent\u27; access to a document database. Currently there are three ongoing document analysis projects at DFKI: INCA, OMEGA, and PASCAL2000/PASCAL+. Though the projects pursue different goals in different application domains, they all share the same problems which have to be resolved with similar techniques. For that reason the activities in these projects are bundled to avoid redundant work. At DFKI we have divided the problem of document analysis into two main tasks, text recognition and text analysis, which themselves are divided into a set of subtasks. In a series of three research reports the work of the document analysis and office automation department at DFKI is presented. The first report discusses the problem of text recognition, the second that of text analysis. In a third report we describe our concept for a specialized document analysis knowledge representation language. The report in hand describes the activities dealing with the text recognition task. Text recognition covers the phase starting with capturing a document image up to identifying the written words. This comprises the following subtasks: preprocessing the pictorial information, segmenting into blocks, lines, words, and characters, classifying characters, and identifying the input words. For each subtask several competing solution algorithms, called specialists or knowledge sources, may exist. To efficiently control and organize these specialists an intelligent situation-based planning component is necessary, which is also described in this report. It should be mentioned that the planning component is also responsible to control the overall document analysis system instead of the text recognition phase onl

Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues

Recognizing scene text is a challenging problem, even more so than the recognition of scanned documents. This problem has gained significant attention from the computer vision community in recent years, and several methods based on energy minimization frameworks and deep learning approaches have been proposed. In this work, we focus on the energy minimization framework and propose a model that exploits both bottom-up and top-down cues for recognizing cropped words extracted from street images. The bottom-up cues are derived from individual character detections from an image. We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them. These interactions are top-down cues obtained from a lexicon-based prior, i.e., language statistics. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets, namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word, and show better performance than comparable methods. We perform a rigorous analysis of all the steps in our approach and analyze the results. We also show that state-of-the-art convolutional neural network features can be integrated in our framework to further improve the recognition performance

Access to recorded interviews: A research agenda

Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed

Document image analysis and recognition: a survey

This paper analyzes the problems of document image recognition and the existing solutions. Document recognition algorithms have been studied for quite a long time, but despite this, currently, the topic is relevant and research continues, as evidenced by a large number of associated publications and reviews. However, most of these works and reviews are devoted to individual recognition tasks. In this review, the entire set of methods, approaches, and algorithms necessary for document recognition is considered. A preliminary systematization allowed us to distinguish groups of methods for extracting information from documents of different types: single-page and multi-page, with text and handwritten contents, with a fixed template and flexible structure, and digitalized via different ways: scanning, photographing, video recording. Here, we consider methods of document recognition and analysis applied to a wide range of tasks: identification and verification of identity, due diligence, machine learning algorithms, questionnaires, and audits. The groups of methods necessary for the recognition of a single page image are examined: the classical computer vision algorithms, i.e., keypoints, local feature descriptors, Fast Hough Transforms, image binarization, and modern neural network models for document boundary detection, document classification, document structure analysis, i.e., text blocks and tables localization, extraction and recognition of the details, post-processing of recognition results. The review provides a description of publicly available experimental data packages for training and testing recognition algorithms. Methods for optimizing the performance of document image analysis and recognition methods are described.The reported study was funded by RFBR, project number 20-17-50177. The authors thank Sc. D. Vladimir L. Arlazarov (FRC CSC RAS), Pavel Bezmaternykh (FRC CSC RAS), Elena Limonova (FRC CSC RAS), Ph. D. Dmitry Polevoy (FRC CSC RAS), Daniil Tropin (LLC “Smart Engines Service”), Yuliya Chernysheva (LLC “Smart Engines Service”), Yuliya Shemyakina (LLC “Smart Engines Service”) for valuable comments and suggestions

Document preprocessing and fuzzy unsupervised character classification

This dissertation presents document preprocessing and fuzzy unsupervised character classification for automatically reading daily-received office documents that have complex layout structures, such as multiple columns and mixed-mode contents of texts, graphics and half-tone pictures. First, the block segmentation algorithm is performed based on a simple two-step run-length smoothing to decompose a document into single-mode blocks. Next, the block classification is performed based on the clustering rules to classify each block into one of the types such as text, horizontal or vertical lines, graphics, and pictures. The mean white-to-black transition is shown as an invariance for textual blocks, and is useful for block discrimination. A fuzzy model for unsupervised character classification is designed to improve the robustness, correctness, and speed of the character recognition system. The classification procedures are divided into two stages. The first stage separates the characters into seven typographical categories based on word structures of a text line. The second stage uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. A fuzzy model of unsupervised character classification, which is more natural in the representation of prototypes for character matching, is defined and the weighted fuzzy similarity measure is explored. The characteristics of the fuzzy model are discussed and used in speeding up the classification process. After classification, the character recognition procedure is simply applied on the limited versions of the fuzzy prototypes. To avoid information loss and extra distortion, an topography-based approach is proposed to apply directly on the fuzzy prototypes to extract the skeletons. First, a convolution by a bell-shaped function is performed to obtain a smooth surface. Second, the ridge points are extracted by rule-based topographic analysis of the structure. Third, a membership function is assigned to ridge points with values indicating the degrees of membership with respect to the skeleton of an object. Finally, the significant ridge points are linked to form strokes of skeleton, and the clues of eigenvalue variation are used to deal with degradation and preserve connectivity. Experimental results show that our algorithm can reduce the deformation of junction points and correctly extract the whole skeleton although a character is broken into pieces. For some characters merged together, the breaking candidates can be easily located by searching for the saddle points. A pruning algorithm is then applied on each breaking position. At last, a multiple context confirmation can be applied to increase the reliability of breaking hypotheses

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Supporting Multi-Domain Model Management

Model-driven engineering has been used in different domains such as software engineering, robotics, and automotive. This approach has models as the primary artifacts, and it is expected to improve quality of system specification and design, as well as the communication among the development team. Managing models that belong to the same domain might not be a complex task because of the features provided by the available development tools. However, managing interrelated models of different domains is challenging. A robot is an example of such a multi-domain system. To develop it one might need to combine models created by experts from mechanics, electronics and software domains. These models might be created using domain specific tools of each domain, and a change in one model of one domain might impact a model from a different domain causing inconsistency in the entire system. This thesis therefore aims to facilitate the evolution of the models in this multi-domain setting. It starts with a systematic literature review in order to identify the open issues, and strategies used to manage models from different domains. We identified that making explicit the relationship between models from different domains can support the models maintenance, making it easy to recognize affected models because of a change. The following step was to investigate ways of extracting information from different engineering models that were created using different modeling notations. For this goal, we required a uniform approach that would be independent from the peculiarities of the notations. This uniform approach can only be based on elements typically present in various modeling notations, i.e., text, boxes, and lines. Thus, we investigated the suitability of optical character recognition (OCR) for extracting textual elements from models from different domains. We also identified the common errors made by the off-the-shelf OCR services, and we proposed two approaches to correct one of these errors. After that, we used name matching techniques on the textual elements extracted by OCR to identify relationships between models from different domains. To conclude, we created an infrastructure that combines all the previous elements into one single tool that can also store the relationships in a structured manner making it easier to maintain the consistency of an entire system. We evaluated it by means of an observational study with a multidisciplinary team that builds autonomous robots designed to play football

Advancing Multi-Modal Deep Learning: Towards Language-Grounded Visual Understanding

Using deep learning, computer vision now rivals people at object recognition and detection, opening doors to tackle new challenges in image understanding. Among these challenges, understanding and reasoning about language grounded visual content is of fundamental importance to advancing artificial intelligence. Recently, multiple datasets and algorithms have been created as proxy tasks towards this goal, with visual question answering (VQA) being the most widely studied. In VQA, an algorithm needs to produce an answer to a natural language question about an image. However, our survey of datasets and algorithms for VQA uncovered several sources of dataset bias and sub-optimal evaluation metrics that allowed algorithms to perform well by merely exploiting superficial statistical patterns. In this dissertation, we describe new algorithms and datasets that address these issues. We developed two new datasets and evaluation metrics that enable a more accurate measurement of abilities of a VQA model, and also expand VQA to include new abilities, such as reading text, handling out-of-vocabulary words, and understanding data-visualization. We also created new algorithms for VQA that have helped advance the state-of-the-art for VQA, including an algorithm that surpasses humans on two different chart question answering datasets about bar-charts, line-graphs and pie charts. Finally, we provide a holistic overview of several yet-unsolved challenges in not only VQA but vision and language research at large. Despite enormous progress, we find that a robust understanding and integration of vision and language is still an elusive goal, and much of the progress may be misleading due to dataset bias, superficial correlations and flaws in standard evaluation metrics. We carefully study and categorize these issues for several vision and language tasks and outline several possible paths towards development of safe, robust and trustworthy AI for language-grounded visual understanding