nils-herrmann/clean_scirepeval huggingface dataset:
Multi-scale texture-based text recognition in ancient manuscripts

Text recognition in ancient documents poses specific challenges such as degradation and staining, fading out of ink, fluctuating text lines, superimposing of text-elements or varying layouts, amongst others. To cope with those challenges, a texture-based approach is proposed, which exploits the fact that different kinds of textures have distinct orientation distributions. The orientation information is extracted using the Auto-Correlation Function (ACF). The approach is applied to three different manuscripts, namely to Glagolitic manuscripts of the 11th century, a Latin and a composite Latin-German manuscript, both originating from the 14th century. The evaluation is based on manually labeled ground truth and shows the accuracy of the features chosen even when the method is applied to document pages that are different in writing style and line spacing to those in the training set.
A Portable High Resolution Imaging System for Digitizing Large-Surface Paintings

Digitizing large-surface paintings at a high resolution in museums is necessary in the field of painting conservation to document the actual condition of paintings (e.g. colour measurements) and for analysis (e.g. to analyse underdrawings in high-resolution infrared images). Hence, this paper presents a portable system that is able to scan large-surface paintings or images to provide high-resolution digital images. Additional a 3D-model of the surface of the scanned object is generated. The purpose of the 3D-model is to use the 3D-information to create a planar representation of a once planar painting or image that was deformed through environmental effects. Otherwise uncorrected images lead to blurring in the overlap region of the blended subimages. The resolution of the final image is up to 33 pixel/mm and the size of the scanned object is up to 1.5 m x 1.3 m.
Regularized single-image super-resolution based on progressive gradient estimation

Gradient domain optimization is widely used in regularized image super-resolution, in which the gradient of high resolution (HR) is estimated for calculating the regularization energy. In this paper, a progressive gradient estimation (PGE) is proposed. In PGE, the gradient of the reconstructed HR image in the previous round of optimization is taken as the estimated gradient in the current round. Then, the estimated image gradient is progressively improved. When the estimated image gradient converges, a high quality HR image can be reconstructed. Experimental results show that the reconstructed HR images by PGE have good qualitative and quantitative performances.
Virtual reconstruction of broken and unbroken pottery

Motivated by the requirements of the present archaeology, we are developing an automated system for archaeological classification and reconstruction of ceramics. Due to the nature of ceramics, most of the excavated vessels are in the form of fragments called sherds. Only a few of the finds are complete, however these finds are the most important and interesting ones. Therefore we are developing a system that handles both complete and broken vessels using two different reconstruction strategies: A shape from silhouette based method for complete vessels and a profile based method for fragments. The profile is the cross-section of the fragment in the direction of the rotational axis of symmetry and can be represented by a closed curve in the plane. For complete vessels the 3D reconstruction is based on a sequence of images of the object taken from different viewpoints. Then the output of both algorithms is used to construct the 3D model of the vessel for classification and display.
Location tracker for a mobile robot

This paper presents landmark based self-localization of a two-wheeled differential drive autonomous mobile robot in a known but highly dynamic environment. The robot is equipped with a pivoted stereo vision system, two digital encoders, a gyro sensor, two lOg accelerometers and a magnetic compass. Global position of the robot is estimated using range measurements of distinct features such as color transitions, corners, junctions and line intersections in the robot environment. However, due to scarcity of distinct features, it is not possible to extract the minimum required features for global position estimation from everywhere in the state space. Therefore, the robot position is tracked between intermittent global localization to have an all time position estimate available to the robot. The robot observation vector is composed of range and bearing measurements of distinct features in the robot environment which is merged with the current position estimate to suppress the accumulating errors. Simulation results illustrate the performance of the location tracker.
Contrast Enhancement in Multispectral Images by Emphasizing Text Regions

This paper deals with the enhancement of the readability in historic texts written on parchment. Due to mold, air, humidity, water, etc. parchment and text are partially damaged and consequently hard to read. In order to enhance the readability of the text, the manuscript pages are imaged in different spectral bands ranging from 360 to 1000 nm. The readability enhancement is based on a spectral and spatial analysis of the multivariate image data by multivariate spatial correlation. The main advantage of the method is that especially the text regions are enhanced which is provided by generating a mask image. This mask is based on the automatic reconstruction of the ruling scheme of the text pages. The method is tested on two medieval Slavonic manuscripts written on parchment.
Single landmark based self-localization of mobile robots

In this paper we discuss landmark based absolute localization of tiny autonomous mobile robots in a known environment. Landmark features are naturally occurring as it is not allowed to modify the environment with special navigational aids. These features are sparse in our application domain and are frequently occluded by other robots. This makes simultaneous acquisition of two or more landmarks difficult. Therefore, we propose a system that requires a single landmark feature. The algorithm is based on range measurement of a single landmark from two arbitrary points whose displacement can be measured using dead-reckoning sensors. Range estimation is done with a stereo vision system. Simulation results show that the robot can localize itself if it can estimates range of the same landmark from two different position and if the displacement between the two position is known.
Automatic reading of analog display instruments

A general design strategy based on experiences of a successful application project concerning the reading of analog devices in order to automate the reading process is presented. The problem space is defined and a description language is introduced which controls the interpretation of specific types of instruments and decouples recognition of primitiva and formation of an aspired result. Furthermore, the interaction between Hough transform or line detection, the user-controlled computation of the measurement and the extension to other measuring instruments is described.
End-to-End Text Recognition Using Local Ternary Patterns, MSER and Deep Convolutional Nets

Text recognition in natural scene images is an application for several computer vision applications like licence plate recognition, automated translation of street signs, help for visually impaired people or image retrieval. In this work an end-to-end text recognition system is presented. For detection an AdaBoost ensemble with a modified Local Ternary Pattern (LTP) feature-set with a post-processing stage build upon Maximally Stable Extremely Region (MSER) is used. The text recognition is done using a deep Convolution Neural Network (CNN) trained with backpropagation. The system presented outperforms state of the art methods on the ICDAR 2003 dataset in the text-detection (F-Score: 74.2%), dictionary-driven cropped-word recognition (F-Score: 87.1%) and dictionary-driven end-to-end recognition (F-Score: 72.6%) tasks.
Color classification of archaeological fragments

We are developing an automated classification and reconstruction system for archaeological fragments. The goal is to relate different fragments belonging to the same vessel based on shape, material and color, thus the color information is important in the pre-classification process. In this work a color specification technique is proposed, which exploits the fact that the spectral reflectance of materials like archaeological fragments vary slowly. We explain how the acquisition system is calibrated in order to get accurate colorimetric information with respect to archaeological requirements. Experimental results are presented for archaeological objects and for a set of test color patches.
Automatic Surveying of Cutaneous Hemangiomas

This paper presents a method for the fully automatic surveying of cutaneous hemangiomas by means of a hemangioma segmentation and a ruler visible in the images. The algorithm computes the spatial resolution of an image. Hemangioma segmentation is accomplished by a single-layer perceptron classification by means of pixel color features. The algorithm was evaluated on a set of 120 images. It achieves satisfactory results on images with clearly visible, saturated hemangiomas
Comparison of reconstruction and example-based Super-Resolution

By comparing two classes of Super-Resolution (SR) namely Example-Based Super-Resolution (EBSR) and Reconstruction-Based Super-Resolution (RBSR), we investigate two points: Firstly, which SR technique EBSR or RBSR will produce SR image that preserves Structure Similarity (SSIM) to the original image? Secondly, which SR technique will produce SR image that is more appealing to human eyes? For resultant SR image, EBSR predicts the relation between high and low frequencies in an image, whereas RBSR algorithms rely on a sequence of frames. From the experimental results on test images, we find that compared to RBSR, EBSR preserves the structure of the original image. Knowing this capability is important for detection and recognition systems. In terms of visual appearance, RBSR is preferred except when there are large motions in consecutive frames. Moreover, the aliasing artifacts cannot be removed by EBSR algorithms.
Ancient document analysis based on text line extraction

In order to preserve our cultural heritage and for automated document processing libraries and national archives have started digitizing historical documents. In the case of degraded manuscripts (e.g. by mold, humidity, bad storage conditions) the text or parts of it can disappear. The remaining parts of the text can be segmented and the ruling can be extrapolated with the a priori knowledge. Since the ruling defines the position of the text within a page, it can be used for layout analysis and as a basis for the enhancement of the readability. Furthermore, information about the scribe (hand) of the manuscript, its spatiotemporal origin can be gained by analyzing the ruling. This paper presents an algorithm for ruling estimation of Glagolitic texts based on text line extraction and is suitable for degraded manuscripts by extrapolating the baselines with the a priori knowledge of the ruling. The algorithm was tested on 30 pages of the Missale Sinaiticum and the evaluation was based on visual criteria.
Recognizing Glagolitic Characters in Degraded Historical Documents

This paper presents a method for the recognition of Glagolitic characters in degraded historical documents. The Glagolitic character recognition is based on Dense SIFT for which image restoration is proposed as a pre-processing step in order to suppress background noise in degraded documents. Two different methods for image restoration are used which are Total Variation regularization and a new restoration method. Each method performs robustly against background noise while preserving character edges and strokes in the documents defected by stain, bleed through, and faded out ink. The experimental results achieved on three datasets show that by using image restoration as a pre-processing step to Dense SIFT generates better recognition rates for Glagolitic characters in degraded documents.
Scale Space Binarization Using Edge Information Weighted by a Foreground Estimation

The proposed binarization algorithm uses a scale space to avoid the estimation of script size dependent parameters. Due to the continous smoothing from finer to coarse scales, noise such as background clutter is suppressed since coarse scales characterize homogeneous regions of the image. Thus, coarser scales of the scale space can be used as a foreground estimation to apply a weigthing scheme robust against noise present in, for instance carbon copies or ancient and degraded documents. Additionally the information of filled regions is propagated through the scales. The use of integral images for the calculation of the mean, standard deviation and morphological operations allow for an efficient implementation of the method presented. The binarization of each scale is based on changes of the local intensity as proposed by Su et al.
Automated stroke ending analysis for drawing tool classification

This paper proposes a drawing tool recognition method based on features calculated from the shape of stroke endings. The application for this method is to help art historians to identify the drawing tool used for a drawing. Since the style of a drawing depends on the drawing tool used, drawing tool recognition is an important step toward a style analysis. A dominant feature of a drawn stroke is its ending. Several features regarding curvature, proportions etc. are calculated out of the shape of the endings. These features are then used to classify stroke endings with a SVM classifier.
FORMS-Locks: A Dataset for the Evaluation of Similarity Measures for Forensic Toolmark Images

We present a toolmark dataset created using lock cylinders seized during criminal investigations of break-ins. A total number of 197 cylinders from 48 linked criminal cases were photographed under a comparison microscope used by forensic experts for toolmark comparisons. In order to allow an assessment of the influence of different lighting conditions, all images were captured using a ring light with 11 different lighting settings. Further, matching image regions in the toolmark images were manually annotated. In addition to the annotated toolmark images and the annotation tool, extracted toolmark patches are provided for training and testing to allow a quantitative comparison of the performance of different similarity measures. Finally, results from an evaluation using a publicly available state-of-the-art image descriptor based on deep learning are presented to provide a baseline for future publications.
Enhancement of Multispectral Images of Degraded Documents by Employing Spatial Information

This work aims at enhancing ancient and degraded writings, which are captured by MultiSpectral Imaging systems. The manuscripts captured, contain faded out characters and are partly corrupted by mold and hardly legible. Several works have shown that such writings can be enhanced by applying unsupervised dimension reduction tools - like Principal Component Analysis (PCA) or Independent Component Analysis (ICA). In this work the Fisher Linear Discriminate Analysis (LDA) is applied in order to reduce the dimension of the multispectral scan and to enhance the degraded writings. Since Fisher LDA is a supervised dimension reduction tool, it is necessary to label a subset of multispectral data. For this purpose, a semi-automated label generation step is conducted, which is based on an automated detection of text lines. Thus, the approach is not only based on spectral information - like PCA and ICA - but also on spatial information. The method has been tested on two Slavonic manuscripts. A qualitative analysis shows, that the LDA based dimension reduction gains better performance, compared to unsupervised techniques.
Writer Retrieval and Writer Identification Using Local Features

Writer identification determines the writer of one document among a number of known writers where at least one sample is known. Writer retrieval searches all documents of one particular writer by creating a ranking of the similarity of the handwriting in a dataset. This paper presents a method for writer retrieval and writer identification using local features and therefore the proposed method is not dependent on a binarization step. First the local features of the image are calculated and with the help of a predefined codebook an occurrence histogram can be created. This histogram is compared to determine the identity of the writer or the similarity of other handwritten documents. The proposed method has been evaluated on two datasets, namely the IAM dataset which contains 650 writers and the Trigraph Slant dataset which contains 47 writers. Experiments have shown that it can keep up with previous writer identification approaches. Regarding writer retrieval it outperforms previous methods.
Layout Analysis for Historical Manuscripts Using Sift Features

We propose a layout analysis method for historical manuscripts that relies on the part-based identification of layout entities. A layout entity -- such as letters of the text, initials or headings -- is composed of a set of characteristic segments or structures, which is dissimilar for distinct classes in the manuscripts under consideration. This fact is exploited in order to segment a manuscript page into homogeneous regions. Historical documents traditionally involve challenges such as uneven writing support and varying shapes of characters, fluctuating text lines, changing scripts and writing styles, and variance in the layout itself. Hence, a part-based detection of layout entities is proposed using a multi-stage algorithm for the localization of the entities, based on interest points. Results show that the proposed method is able to locate initials, headings and text areas in ancient manuscripts containing stains, tears and partially faded-out ink sufficiently well.
A hybrid approach towards vision based self-localization of autonomous mobile robots

This paper presents a hybrid approach towards self- localization of tiny autonomous mobile robots in a known but highly dynamic environment. The proposed algorithm is intended for two-wheeled differential drive robots which are equipped with a pivoted stereo vision system, two digital encoders, a gyro sensor, two 10g accelerometers and a magnetic compass. The global position of the robot can be estimated by extracting two distinct landmarks from the robot environment and measuring their range and orientation using the stereo vision system. However, distinct landmarks are not available through the entire state space and it is required to track the robot position once a global estimate is available. Tracking of the globally estimated position is performed within the framework of extended Kalman filter. Constant monitoring of the robot observation enables it to detect any unexpected situation. Simulation results show that robot can successfully localize itself at startup and is capable of detecting and recovering from localization failures.
Combining shape from silhouette and shape from structured light for volume estimation of archaeological vessels

An algorithm for the automatic construction of a 3d model of archaeological vessels using two different 3d algorithms is presented. In archeology the determination of the exact volume of arbitrary vessels is of importance since this provides information about the manufacturer and the usage of the vessel. To acquire the 3d shape of objects with handles is complicated, since occlusions of the object's surface are introduced by the handle and can only be resolved by taking multiple views. Therefore, the 3d reconstruction is based on a sequence of images of the object taken from different viewpoints with two different algorithms; shape from silhouette and shape from structured light. The output of both algorithms are then used to construct a single 3d model. Results of the algorithm developed are presented for both synthetic and real input images.
Octree-based fusion of shape from silhouette and shape from structured light

An algorithm for the automatic construction of a 3d model of archaeological vessels using two different 3d algorithms is presented. In archeology the determination of the exact volume of arbitrary vessels is of importance since this provides information about the manufacturer and the usage of the vessel. To acquire the 3d shape of objects with handles is complicated, since occlusions of the object's surface are introduced by the handle and can only be resolved by taking multiple views. Therefore, the 3d reconstruction is based on a sequence of images of the object taken from different viewpoints with different algorithms; shape from silhouette and shape from structured light. The output of both algorithms are then used to construct a single 3d model. Results of the algorithm developed are presented for both synthetic and real input images.
Learning a similarity measure for striated toolmarks using convolutional neural networks

We propose TripNet a method for calculating similarities between striated toolmarks. The objective for this system is to distinguish the individual characteristics of tools while being invariant to class and sub-class characteristics, and varying parameters like angle of attack. Instead of designing a handcrafted feature extractor customized for this task we propose the use of a Convolutional Neural Network (CNN). With the proposed system 1D profiles extracted from images of striated toolmarks are mapped into an embedding. The system is trained by minimizing a triplet loss function so that a similarity measure is defined by the L2 distance in this embedding. The performance is evaluated on the NFI Toolmark database containing 300 striated toolmarks of screwdrivers published by the National Forensic Institute of the Netherlands. The proposed system is able to adapt to a large range of angles of attack between 15°and 75°, achieving a Mean Average Precision (MAP) of 0.95 for toolmark comparisons with differences in angle of attack of 15°to 45°; for differences of 15°to 60°a MAP of 0.78 is achieved.
Evaluation of Methods for Optical 3-D Scanning of Human Pinnas

In the context of computational acoustics, a detailed evaluation of various 3-D scanning methods for the purpose of capturing the geometry of the human pinna (the visible part of the ear) is presented. Over 80 full 3-D scans of the head and ears of three subjects were performed with six different 3-D scanning methods, including photogrammetry with nine different camera and texturing options. We provide a numerical comparison of the scanning performance in terms of accuracy and completeness, and show the effect of ambient occlusion on the accuracy. The numerical and practical issues for scanning the ears for computational acoustics are discussed.
Detecting Text Areas and Decorative Elements in Ancient Manuscripts

An approach for the detection of decorative elements – such as initials and headlines – and text regions, focused on ancient manuscripts, is presented. Due to their age, ancient manuscripts suffer from degradation and staining as well as ink is faded-out over the time. Identifying decorative elements and text regions allows indexing a manuscript and serves as input for Optical Character Recognition (OCR) as it localizes regions of interest within document pages. We propose a robust method inspired by state-of-the-art object recognition methodologies. Scale Invariant Feature Transform (SIFT) descriptors are chosen to detect the regions of interest, and the scale of the interest points is used for localization. The classification is based on the fact that local properties of the decorative elements are different to those of regular text. The results show that the method is able to locate regular text in ancient manuscripts. The detection rate of decorative elements is not as high as for regular text but already yields to promising results.