We present a method for transcribing historical newsprint images. To begin, for training and evaluation, we created a corpus of human-generated transcripts for almost 38,000 image snippets which contains nearly five million words. This may be one of the largest corpora of transcribed historical newsprint ever created. Then, we developed our automatic transcription process by leveraging the pattern recognition and statistical components of the state-of-the-art speech recognition toolkit, Kaldi. Specifically, we modified its language model behavior and we replaced Kaldi's speech-to-features transformation components with our own image-to-features process. Our replacement components include the use of word partials; image rotation; line segmentation which extends state-of-the-art methods; and customized feature generation. We conduct two evaluations of our technology: (a) an evaluation based on random selections of newspaper snippets; and (b) a diachronic evaluation of newspaper snippets by time frame. We compare our results of these evaluations to those of the commercial engines ABBYY Fine Reader Version 12 and OmniPage 18, as well as to the freely available system, Tesseract. We demonstrate that our process typically yields accuracies which are comparable to or exceed the accuracies of these other engines.
This article presents a system dedicated to automatic language identification of text regions in heterogeneous and complex documents. This system is able to process documents with mixed printed and handwritten text and various layouts. To handle such a problem, the authors propose a system that performs the following sub-tasks: writing type identification (printed/handwritten), script identification and language identification. The methods for writing type recognition and script discrimination are based on analysis of the connected components, while the language identification approach relies on a statistical text analysis, which requires a recognition engine. The authors evaluate the system on a new public dataset and present detailed results on the three tasks. Their system outperforms the Google plug-in evaluated on ground-truth transcriptions of the same dataset. (C) 2016 Society for Imaging Science and Technology.
Recent advances in writer identification push the limits by using increasingly complex methods relying on sophisticated preprocessing, or the combination of already complex descriptors. In this paper, we pursue a simpler and faster approach to writer identification, introducing novel descriptors computed from the geometrical arrangement of interest points at different scales. They capture orientation distributions and geometrical relationships of script parts such as strokes, junctions, endings, and loops. Thus, we avoid a fixed set of character appearances as in standard codebook-based methods. The proposed descriptors significantly cut down processing time compared to existing methods, are simple and efficient, and can be applied out-of-the-box to an unseen dataset. Evaluations on widely-used datasets show their potential when applied by themselves, and in combination with other descriptors. Limitations of our method relate to the amount of data needed to obtain reliable models.
Detecting overlapping text from map images is a challenging problem. Previous algorithms generally assume specific cartographic styles (e. g., road shapes and text format) and are difficult to adjust for handling different map types. In this paper, we build on our previous text recognition work, Strabo, to develop an algorithm for detecting overlapping characters from non-text symbols. We call this algorithm Overlapping Text Detection (OTD). OTD uses the recognition results and locations of detected text labels (from Strabo) to detect potential areas that contain overlapping text. Next, OTD classifies these areas as either text or non-text regions based on their shape descriptions (including the ratio of number of foreground pixels to area size, number of connected components, and number of holes). The average precision and recall of OTD in classifying text and non-text regions were 77% and 86%, respectively. We show that OTD improved the precision and recall of text detection in Strabo by 19% and 41%, respectively, and produced higher accuracy compared to a state-of- the-art text/graphic separation algorithm.
We propose a human-in-the-loop scheme for optical music recognition. Starting from the results of our recognition engine, we pose the problem as one of constrained optimization, in which the human can specify various pixel labels, while our recognition engine seeks an optimal explanation subject to the humansupplied constraints. In this way we enable an interactive approach with a uniform communication channel from human to machine where both iterate their roles until the desired end is achieved. Pixel constraints may be added to various stages, including staff finding, system identification, and measure recognition. Results on a test show significant speed up when compared to purely human-driven correction. Introduction Optical Music Recognition (OMR) holds potential to transform score images into symbolic music libraries, thus enabling search, categorization, and retrieval by symbolic content, as we now take for granted with text. Such symbolic libraries would serve as the foundation for the emerging field of computational musicology, and provide data for a wide variety of fusions between music, computer science, and statistics. Equally exciting are applications such as the digital music stand, and systems that support practice and learning through objective analysis of rhythm and pitch. In spite of this promise, progress in OMR has been slow; even the best systems, both commercial and academic, leave much to be desired[7]. In many cases the effort needed to correct OMR output may be more than that of entering the music data from scratch[8]. In such cases OMR systems fail to make any meaningful contribution at all. The reason for these disappointing results is simply that OMR is hard. Bainbridge [17] discusses some the challenges of OMR that impede its development. One central problem is that music notation contains a large variety of somewhat-rare musical symbols and conventions [4], such as articulations, bowings, tremolos, fingerings, accents, harmonics, stops, repeat marks, 1st and 2nd endings, dal segno and da capo markings, trills, mordants, turns, breath marks, etc. While one can easily build recognizers that accommodate these somewhat-unusual symbols and special notational cases, the false positive detections that result often outweigh the additional correct detections they produce. Under some circumstances, some not-so-rare symbols fall into this better-not-to-recognize category, such as augmentation dots, double sharps, and partial beams. Another issue arises from the difficulty in describing the high-level structure of music notation. Objects such as chords, beamed groups, and clef-key-signatures, are highly structured and lend themselves naturally to grammatical representation, however, the overall organization of symbols within a measure is far less constrained. The OMR literature contains several efforts to formulate a unified grammar for music notation [10, 11]. These approaches represent grammars of primitive symbols (beams, flags, note heads, stems, etc.) and begin by assuming a collection of segmented primitives. While our grammars have significant overlap with these approaches, one of our primary uses for the grammar is the segmentation of the symbols into primitives — we do not believe it is realistic to identify the primitives without understanding the larger structures that contain them. Kopec [12] describes a compelling Markov Source Model for music recognition that simultaneously segments and recognizes. However, the approach addresses a small subset of music notation and does not generalize in any obvious way. In particular, our primary focus is on the International Music Score Library Project (IMSLP), while Kopec’s model covers a small minority of the examples encountered there. Other difficulties stem from the kinds of image degradation encountered, including poor or variable contrast, skew and warping of an image caused when the document is not aligned or flat in the scanner bed, hand-written marks, damage to pages, as well as other sources. Some recent research has been dedicated to the improvement of fully automated OMR systems in post-process fashion, or other ways that leave the core recognition engine intact. These efforts either create systems that adapt automatically [16, 24], add musically meaningful constraints for recognition [1, 5], or combine multiple recognizers to achieve better accuracy [9, 7]. However, OMR research is still a long way from our shared goal of creating large scale symbolic music databases. Hankinson et al. [15] created a prototype system for distributed large-scale OMR, which converts a collection of Gregorian chant scores into symbolic files to facilitate their in situ content-based retrieval, though the approach still requires a large amount of careful proofreading and correction. In light of these many obstacles and our collective past history, it seems unwise to bet on fully automated OMR systems that will produce high-quality results with any consistency. Instead we favor casting the problem as an interactive one, thus putting the human in the computational loop. In this case the essential challenge becomes one of minimizing the user’s effort, putting as much burden as possible on the computer, (but no more). There are many creative ways to integrate a person into the recognition pipeline, allowing her to correct, give hints, or direct the computation. This work constitutes an effort in this direction. Our first attempt to bring the human into OMR pipeline built a user interface allowing the correction of individual primitives: stem, beam, note head, single flag, sharp, augmentation dot, etc. Thus the user’s task was simply to cover the image ink by adding and deleting appropriate primitives. A benefit of this approach is that it presents the user with a clearly-defined task that doesn’t require knowledge of the system’s inner workings. There are, however, several weaknesses to this approach: the human tagging process is laborious; it fails to provide important syntactic relations between primitives; it requires the person to precisely register the primitive with the image; and it allows the person to create uninterpretable configurations of primitives (say a stem with no note head) creating havoc further down the OMR pipeline. Our aim here is to improve on all these weaknesses while still presenting a simple task to the user. Our current approach first presents the user with the original recognition results, obtained through fully automatic means. The user may then label any individual pixel according to the recognition task at hand. For instance, during system recognition the user may label a pixel as white space or bar line, while during measure recognition we use a richer collection of labels including, closed/half/whole note head, stem, ledger line, beam, sharp, single flag, etc. The system then re-recognizes subject to the user-imposed constraint. Since our recognizers embed highly restrictive assumptions on the primitives they assemble, a single correction often fixes a number of problems at once. Human and machine then iterate the process of providing and synthesizing human-supplied constraints into recognized results. This approach leaves the registration problem — the precise location of primitives — in the hands of the machine, where we believe it belongs. Furthermore, since our system can only recognize meaningful configurations of symbols, we avoid the problem of trying to assemble human-tagged composite symbols that may not make sense. While the resulting process may still be laborious, our results indicate that the human burden can be reduced considerably by employing this strategy. Furthermore, there are many other ways of introducing human-specified constraints into the recognition process, thus the current effort constitutes an initial exploration of a longer-term goal. Interactive OMR Various authors, such as Rebelo [13], suggest that interactive OMR system could be a realistic solution to the problem, though the central challenge of fusing the human and machine contributions still remains open. Human-in-the-loop computation has received considerable attention recently [23]. It has been applied to a wide variety of areas, such as retrieval systems [19], object classification [20], character recognition [18], document indexing [25], image labeling [22] and fined-grained visual categorization [21]. Romero [26] proposed a Hidden Markov Model (HMM) for computer-assisted text transcription, in which the user-imposed prefix is used to constrain both the sequence decoding and language priors. The potential of all these different applications is summarized in von Ahn’s statement [18]: “Human processing power can be harnessed to solve problems that computer cannot yet solve.” There have already been several OMR systems taking into account human-in-the-loop computation. For instance, Fujinaga [4] proposed an adaptive system that could incrementally improve its symbol classifiers based on human feedback. Church [6] implemented an interface accepting user feedback to guide misrecognized measures toward similar correct measures found elsewhere in the score. Our system uses human feedback in an entirely different manner — as a means of constraining the recognition process in a user-specified manner, thus leveraging the user’s input in the heart of the system. It is worth noting that our approach constitutes a generic framework that poses human-in-theloop recognition as constrained optimization, applicable beyond the specific confines of OMR. Human-Directed Recognition As motivation consider the example given in Figure 1. Suppose our recognition misses the upper note head of the chord (Figure 1b). Then suppose the user labels a single pixel that belongs to the missing note head as solid head (Figure 1c). When the system re-recognizes subject to this constraint, the note head, its associated ledger line, accidental and stem portion may all b
Now more and more people release their resumes through the Internet, and PDF is a wide adopted format of resume documents which contain lots of valuable information for recruitment, personal profile mining, etc. However, only a few studies have been down in this direction. Therefore, this paper focuses on the task–information extraction from resume documents in PDF format, and proposes a hierarchical extraction method. At first, this method segments a page into blocks according to heuristic rules. And then each block is classified by a Conditional Random Field (CRF) model. To take advantage of the structure and layout information of PDF documents, the classification model employs two kinds of features:content-based features and layoutbased features which are parsed from PDF documents. The experimental results show that the effectiveness of the proposed method. Especially, the layout-based features are proved to be very useful for the task, improving more than 20 percent of the average F1-score in the experiments.
Extracting strokes from handwriting in historical documents provides high-level features for the challenging problem of handwriting recognition. Such handwriting often contains noise, faint or incomplete strokes, strokes with gaps, overlapping ascenders and descenders and competing lines when embedded in a table or form, making it unsuitable for local line following algorithms or associated binarization schemes. We introduce Intelligent Pen for piece-wise optimal stroke extraction. Extracted strokes are stitched together to provide a complete trace of the handwriting. Intelligent Pen formulates stroke extraction as a set of piece-wise optimal paths, extracted and assembled in cost order. As such, Intelligent Pen is robust to noise, gaps, faint handwriting and even competing lines and strokes. Intelligent Pen traces compare closely with the shape as well as the order in which the handwriting was written. A quantitative comparison with an ICDAR handwritten stroke data set shows Intelligent Pen traces to be within 2.58 pixels (mean difference) of the manually created strokes.
Chat-logs are informative documents available to nowadays social network providers. Providers and law enforcement tend to use these huge logs anonymously for automatic online Sexual Predator Identification (SPI) which is a relatively new area of application. The task plays an important role in protecting children and juveniles against being exploited by online predators. Pattern recognition techniques facilitate automatic identification of harmful conversations in cyber space by law enforcements. These techniques usually require a large volume of high-quality training instances of both predatory and non-predatory documents. However, collecting non-predatory documents is not practical in real-world applications, since this category contains a large variety of documents with many topics including politics, sports, science, technology and etc. We utilized a new semi-supervised approach to mitigate this problem by adapting an anomaly detection technique called One-class Support Vector Machine which does not require non-predatory samples for training. We compared the performance of this approach against other state-of-the-art methods which use both positive and negative instances. We observed that although anomaly detection approach utilizes only one class label for training (which is a very desirable property in practice); its performance is comparable to that of binary SVM classification. In addition, this approach outperforms the classic two-class Naïve Bayes algorithm, which we used as our baseline, in terms of both classification accuracy and precision.
Motivation: Handwriting datasets may contain specimens assigned to the wrong writer. A little discussed problem, such misclassifications, "cuckoos", can bias recognition, retrieval, identification, and other expertise systems, with serious consequences in biometric and forensic applications. Indeed, misclassification research has been purported as the most important topic in pattern recognition. Objective: We describe the design of a generic semi-automatic method for detecting possible misclassifications and illustrate it by way of an exemplary classification criteria (writer identity), measurement feature (contour orientation), and document distance metrics combination. Method: The core of the method consists in automated ranking of writer classes by stylistic variability, using the open source software Alphonse, followed by visual inspection of a limited number of top ranking classes, using an interactive handwriting datasets visualization tool, Rex. The method is independent from dataset producers and does not necessitate training. It is the result of empirical and theoretical research, and its performance demonstrated on the Swiss IAM offline handwriting dataset. Findings: We show that to evaluate the performance of a quality control it is necessary to consider the interdependency between system sensitivity and task difficulty. We propose a dataset-independent measure of the scrambling severity of a dataset and its proneness to misclassification. We find that in a broad writer population the variability of the contour orientation approaches a log-normal distribution, increasing the amount of genuine outliers.
Medical images in biomedical documents tend to be complex by nature and often contain several regions that are annotated using arrows. Arrowhead detection is a critical precursor to regionof-interest (ROI) labeling and image content analysis. To detect arrowheads, images are first binarized using fuzzy binarization technique to segment a set of candidates based on connected component principle. To select arrow candidates, we use convexity defect-based filtering, which is followed by template matching via dynamic programming. The similarity score via dynamic time warping (DTW) confirms the presence of arrows in the image. Our test on biomedical images from imageCLEF 2010 collection shows the interest of the technique.
Calligraphy collections are being scanned into document images for preservation and accessibility. The digitization technology is mature and calligraphy character recognition is well underway, but automatic calligraphy style classification is lagging. Special style features are developed to measure style similarity of calligraphy character images of different stroke configurations and GB (or Unicode) labels. Recognizing the five main styles is easiest when a style-labeled sample of the same character (i. e., same GB code) from the same work and scribe is available. Even samples of characters with different GB codes from same work help. Style classification is most difficult when the training data has no comparable characters from the same work. These distinctions are quantified by distance statistics between the underlying feature distributions. Style classification is more accurate when several character samples from the same work are available. In adverse practical scenarios, when labeled versions of unknown works are not available for training the classifier, Borda Count voting and adaptive classification of style-sensitive feature vectors seven-character from the same work raises the ~70% single-sample baseline accuracy to ~90%.
Optical character recognition software converts an image of text to a text document but typically degrades the document’s contents. Correcting such degradation to enable the document set to be queried effectively is the focus of this work. The described approach uses a fusion of substring generation rules and context aware analysis to correct these errors. Evaluation was facilitated by two publicly available datasets from TREC-5’s Confusion Track containing estimated error rates of 5% and 20% . On the 5% dataset, we demonstrate a statistically significant improvement over the prior art and Solr’s mean reciprocal rank (MRR). On the 20% dataset, we demonstrate a statistically significant improvement over Solr, and have similar performance to the prior art. The described approach achieves an MRR of 0.6627 and 0.4924 on collections with error rates of approximately 5% and 20% respectively. Introduction Documents that are not electronically readable are increasingly difficult to manage, search, and maintain. Optical character recognition (OCR) is used to digitize these documents, but frequently produces a degraded copy. We develop a search system capable of searching such degraded documents. Our approach sustains a higher search accuracy rate than the prior art as evaluated using the TREC-5 Confusion Track datasets. Additionally, the approach developed is domain and language agnostic; increasing its applicability. In the United States there are two federal initiatives underway focused on the digitization of health records. First, the federal government is incentivizing local and private hospitals to switch from paper to electronic health records to improve the quality of care [3]. Second, the Veteran’s Affairs (VA) has an initiative to eliminate all paper health records by 2015 [2]. Both processes require converting paper records to digital images, and – hopefully – indexing of the digitized images to support searching. These efforts either are leveraging or can leverage OCR to query the newly created records to improve quality of service. These are but a few of the many examples demonstrating the importance of OCR. An OCR process is composed of two main parts. First is the conversion of an imagine to text by identifying characters and words from images [8, 17]. Second, the resulting text is post-processed to identify and correct errors during the first phase. Techniques in this process can range from simple dictionary checks to statistical methods. Our research focuses on the latter phase. Some work in the second phase has attempted to optimize the algorithm’s parameters by training algorithms on portions of the dataset [16]. However, such an approach does not generalize to other OCR collections. Other work focuses on specialized situations: handwritten documents [15]; signs, historical markers/documents [13, 9]. While other works hinge on assumptions: the OCR exposes a confidence level for each processed word [7]; online resources will allow the system to make hundredsof-thousands of queries in short bursts [6, 12]; or the ability to crawl many web sources to create lexicons [28]. We focus on the generalized case of post-processing of OCR degraded documents without training or consideration of document type. Historically, there was a flurry of research in this area, particularly around the time TREC released an OCR corrupted dataset [10]. Entries to the TREC competition fell into 2 categories: attempts to clarify or expand the query and attempts to clarify or correct the documents themselves. Results submitted from the latter category have higher mean reciprocal ranks (MRR). Therefore, we continue work in this direction. Taghva et al. published many results in this area [26]. They have designed specialized retrieval engines for OCR copies of severely degraded documents [25] and found their tested OCR error correction methods had little impact on precision/recall vs an unmodified search engine [24]. This result suggests that Solr is a good enough solution to searching OCR corrupted collections. Their most related work to this research was the creation of a correction system for OCR errors. This system uses statistical methods to make more accurate corrections, but requires user training and assistance [27]. More recent work from this lab has been focused on similar supervised approaches [18]. In contrast, our objective is the development of a solution requiring no user intervention or training data. Our contributions are: • Given a minimally corrupted dataset (∼5% error rate), we show that a fusion based method has a statistically significantly (p<0.05) higher MRR than prior art, and higher MRR than individual methods for correcting corrupt words. • Given a moderately corrupted dataset (∼20% error rate), we show the same method’s MRR is roughly equal to the prior art’s. • We evaluate the impact of context when correcting corrupted terms in a corrupted document. • We demonstrate the tradeoffs of occurrence frequency thresholds for corrupt words. Thresholds set too high and too low negatively impact MRR. • We evaluate filtering methods to increase the accuracy of identifying corrupt words. • We reinforce the assumption that use of domain keywords improve correction rates by showing their impact on MRR. Methods Dataset Document Set The experiments performed are based on the publicly available TREC-5 Confusion Track collection: 395 MB containing approximately 55,600 documents. The documents are part of the Federal Register printed by the United States Government Printing Office. A list of 49 queries and the best resulting document are provided for evaluation. Since each query seeks only a single document, MRR is reported. TREC created two corrupted datasets from the original collection with an estimated 5% error rate and 20% error rate. Real Words Dictionary We create an exhaustive English dictionary of real words using the following three datasets: 1) 99,044 words from the English dictionary1; 2) 94,293 sir names in the United States2; 3) 1,293,142 geographic locations within the United States3. Collectively, this dictionary is referred to as real words. To measure the impact of a domain specific dictionary, we supplement the real words dictionary with additional terms obtained from the 1996 Federal Register [1]. By selecting the publications from 1996 – 2 year after our test set – we ensure minimal possible overlap of temporal topics. To accurately attribute the impact of these domain terms, we report our results both with and without this dataset.
Deep architectures based on convolutional neural networks have obtained state-of-the-art results for several recognition tasks. These architectures rely on a cascade of convolutional layers and activation functions. Beyond the set-up of the number of layers and the number of neurons in each layer, the choice of activation functions, training optimization algorithm and regularization procedure are of great importance. In this work we start from a deep convolutional architecture and we describe the effect of recent activation functions, optimization algorithms and regularization procedures when applied to the recognition of handwritten digits from the MNIST dataset. The network achieves a 0.38 % error rate, matching and slightly improving the best known performance of a single model trained without data augmentation at the time the experiments were performed.
Redundancy of word and sub-word occurrences in large documents can be effectively utilized in an OCR system to improve recognition results. Most OCR systems employ language modeling techniques as a post-processing step; however these techniques do not use important pictorial information that exist in the text image. In case of large-scale recognition of degraded documents, this information is even more valuable. In our previous work, we proposed a sub-word image clustering method for the applications dealing with large printed documents. In our clustering method, the ideal case is when all equivalent sub-word images lie in one cluster. To overcome the issues of low print quality, the clustering method uses an image matching algorithm for measuring the distance between two sub-word images. The measured distance with a set of simple shape features were used to cluster all sub-word images. In this paper, we analyze the effects of adding more shape features on processing time, purity of clustering, and the final recognition rate. Previously published experiments have shown the efficiency of our method on a book. Here we present extended experimental results and evaluate our method on another book with totally different font face. Also we show that the number of the new created clusters in a page can be used as a criteria for assessing the quality of print and evaluating preprocessing phases.
Handwritten tabular documents, such as census, birth, death and marriage records, contain a wealth of information vital to genealogical and related research. Much work has been done in segmenting freeform handwriting, however, segmentation of cursive handwriting in tabular documents is still an unsolved problem. Tabular documents present unique segmentation challenges caused by handwriting overlapping cell-boundaries and other words, both horizontally and vertically, as “ascenders” and “descenders” overlap into adjacent cells. This paper presents a method for segmenting handwriting in tabular documents using a min-cut/max-flow algorithm on a graph formed from a distance map and connected components of handwriting. Specifically, we focus on line, word and first letter segmentation. Additionally, we include the angles of strokes of the handwriting as a third dimension to our graph to enable the resulting segments to share pixels of overlapping letters. Word segmentation accuracy is 89.5% evaluating lines of the data set used in the ICDAR2013 Handwriting Segmentation Contest. Accuracy is 92.6% for a specific application of segmenting first and last names from noisy census records. Accuracy for segmenting lines of names from noisy census records is 80.7%. The 3D graph cutting shows promise in segmenting overlapping letters, although highly convoluted or overlapping handwriting remains an ongoing challenge.
We introduce a new method for indexing and retrieving mathematical expressions, and a new protocol for evaluating math formula retrieval systems. The Tangent search engine uses an inverted index over pairs of symbols in math expressions. Each key in the index is a pair of symbols along with their relative distance and vertical displacement within an expression. Matched expressions are ranked by the harmonic mean of the percentage of symbol pairs matched in the query, and the percentage of symbol pairs matched in the candidate expression. We have found that our method is fast enough for use in real time and finds partial matches well, such as when subexpressions are re-arranged (e.g. expressions moved from the left to the right of an equals sign) or when individual symbols (e.g. variables) differ from a query expression. In an experiment using expressions from English Wikipedia, student and faculty participants (N=20) found expressions returned by Tangent significantly more similar than those from a text-based retrieval system (Lucene) adapted for mathematical expressions. Participants provided similarity ratings using a 5-point Likert scale, evaluating expressions from both algorithms one-at-a-time in a randomized order to avoid bias from the position of hits in search result lists. For the Lucene-based system, precision for the top 1 and 10 hits averaged 60% and 39% across queries respectively, while for Tangent mean precision at 1 and 10 were 99% and 60%. A demonstration and source code are publicly available.
We describe a document image segmentation algorithm to classify a scanned document into different regions such as text/line drawings, pictures, and smooth background. The proposed scheme is relatively independent of variations in text font style, size, intensity polarity and of string orientation. It is intended for use in an adaptive system for document image compression. The principal parts of the algorithm are the generation of the foreground and background layers and the application of hierarchical singular value decomposition (SVD) in order to smoothly fill the blank regions of both layers so that the high compression ratio can be achieved. The performance of the algorithm, both in terms of its effectiveness and computational efficiency, was evaluated using several test images and showed superior performance compared to other techniques.
In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.
In this paper a method to detect the electrical circuit elements from the scanned images of electrical drawings is proposed. The method, based on histogram analysis and mathematical morphology, detects the circuit elements, for example, circuit components, wires, and generates a connectivity matrix which may be used to find similar, but spatially different looking circuit using graph isomorphism. The work may also be used for vectorization of the circuit drawings utilising the information on the segmented circuit elements and corresponding connectivity matrix. The novelty of the method lies in its simplicity and adaptability to work with a tolerable skewed image and the capability to segment symbols irrespective of their orientation. The proposed method is tested over a data-set containing more than one hundred scanned images of a variety of electrical drawings. Some of the results are presented in this paper to show the efficacy and robustness of the proposed method.
In this paper, we propose a new dataset and a ground-truthing methodology for layout analysis of historical documents with complex layouts. The dataset is based on a generic model for ground-truth presentation of the complex layout structure of historical documents. For the purpose of extracting uniformly the document contents, our model defines five types of regions of interest: page, text block, text line, decoration, and comment. Unconstrained polygons are used to outline the regions. A performance metric is proposed in order to evaluate various page segmentation methods based on this model. We have analysed four state-of-the-art ground-truthing tools: TRUVIZ, GEDI, WebGT, and Aletheia. From this analysis, we conceptualized and developed Divadia, a new tool that overcomes some of the drawbacks of these tools, targeting the simplicity and the efficiency of the layout ground truthing process on historical document images. With Divadia, we have created a new public dataset. This dataset contains 120 pages from three historical document image collections of different styles and is made freely available to the scientific community for historical document layout analysis research.
