nils-herrmann/clean_scirepeval huggingface dataset:
Are Characters Objects?

This paper presents a character recognition system that handles degraded manuscript documents like the ones discovered at the St. Catherine’s Monastery. In contrast to state-of-the-art OCR systems, no early decision (image binarization) needs to be performed. Thus, an object recognition methodology is adapted for the recognition of ancient manuscripts. The proposed system is based on local descriptors which are clustered in order to localize characters. Finally, a class probability histogram is assigned to each character present in an image which allows for the character classification. The system achieves an F0.5 score of 0.77 on real world data that contains 13.5% highly degraded characters.
Text Line Detection for Heterogeneous Documents

Text line detection is a pre-processing step for automated document analysis such as word spotting or OCR. It is additionally used for document structure analysis or layout analysis. Considering mixed layouts, degraded documents and handwritten documents, text line detection is still challenging. We present a novel approach that targets torn documents having varying layouts and writing. The proposed method is a bottom up approach that fuses words, to globally minimize their fusing distance. In order to improve processing time and further layout analysis, text lines are represented by oriented rectangles. Even though, the method was designed for modern handwritten and printed documents, tests on medieval manuscripts give promising results. Additionally, the text line detection was evaluated on the ICDAR 2009 and ICFHR 2010 Handwriting Segmentation Contest datasets.
On 3D mosaicing of rotationally symmetric ceramic fragments

A major obstacle to the wider use of 3D object reconstruction and modeling is the extent of manual intervention needed. Such interventions are currently massive and exist throughout every phase of a 3D reconstruction project: collection of images, image management, establishment of sensor position and image orientation, extracting the geometric detail describing an object, merging geometric, texture and semantic data. This work aims to develop a solution for automated documentation of archaeological pottery, which also leads to a more complete 3D model out of multiple fragments. Generally the 3D reconstruction of arbitrary objects from their fragments can be regarded as a 3D puzzle. In order to solve it we identified the following main tasks: 3D data acquisition, orientation of the object, classification of the object and reconstruction. We demonstrate the method and give results on synthetic and real data.
A Survey of Techniques for Document and Archaeology Artefact Reconstruction

An automated assembling of shredded/torn documents (2D) or broken pottery (3D) will support philologists, archaeologists and forensic experts. An automated solution for this task can be divided into shape based matching techniques (apictorial) or techniques that analyze additionally the visual content of the fragments (pictorial). In the case of visual content techniques like texture based analysis are used. Depending on the application, shape matching techniques are suitable for entities of the puzzle problem with small numbers of pieces (e.g. up to 20). Also artefacts like broken and lost pieces or overlapping parts of fragments increase the error rate of shape based techniques since the matching of adjacent boundaries can fail. As a result additional features, e.g. color, document structure, have to be used. This paper presents an overview about current puzzle applications in Cultural Heritage, and introduces also the main problems in puzzle solving.
Estimating the next sensor position based on surface characteristics

In order to reconstruct the viewable surface of an object completely, multiple views of the same object have to be used and integrated into a common coordinate system. One of the major problems of the 3D surface reconstruction using a turntable, is the varying resolution in the direction to the camera, due to the varying distance of object points to the rotational axis of the turntable. To guarantee a uniform object resolution, we calculate the next angle dynamically, depending on the entropy of the surface part actually acquired. To minimize the loss of information and to guarantee a uniform surface resolution, we derive a relation between the entropy and the next viewing angle, based on the profile sections acquired in the last two steps of the acquisition.
Improving OCR Accuracy by Applying Enhancement Techniques on Multispectral Images

This work is concerned with the legibility enhancement of ancient and degraded handwritings. The writings are partially barely visible under normal white light and hence they have been imaged with a MultiSpectral Imaging (MSI) system in order to increase their legibility. Dimension reduction techniques - like Principal Component Analysis (PCA) - can be used to further enhance the contrast of the faded-out characters. In this work the dimensionality of the multispectral scan is lowered, by applying Linear Discriminant Analysis (LDA). Since LDA is a supervised dimension reduction method, it is necessary to label a subset of the multispectral samples as belonging to the fore-or background. For this purpose, an approach is suggested that uses spatial information. The enhancement method is evaluated by Optical Character Recognition (OCR). By applying the enhancement method the OCR performance is increased in the case of degraded writings, compared to OCR results gained on unprocessed multispectral images and to OCR results achieved on images, which have been produced by applying unsupervised dimension reductions.
Robust Skew Estimation of Handwritten and Printed Documents Based on Grayvalue Images

Skew estimation is a preprocessing step in document image analysis to determine the global dominant orientation of a document's text lines. A skew angle can be introduced during scanning, or if a document is photographed. The correction of the skew angle is necessary for further image analysis, to avoid an influence to the performance of skew sensitive methods, e.g. Optical Character Recognition (OCR) or page segmentation. The performance of current skew estimation methods is shown at the ICDAR2013 Document Image Skew Estimation Contest (DISEC), which uses a benchmark dataset of binarized printed documents with varying layouts and languages like English, Chinese or Greek. The proposed method is based on a Focused Nearest Neighbour Clustering (FNNC) of interest points and the analysis of paragraphs/lines and achieved rank 5 at the contest. In this paper it is shown, that the use of gray value images can outperform the results restricted to binarized images, thus the proposed method avoids the binarization step which is still an open research topic in document image analysis. The robustness of the method is also shown on a dataset comprising historical documents and on low resolution images. The method is evaluated on the DISEC dataset and three additional datasets (historical documents, low resolution documents, and machine printed documents).
Event-driven feature analysis in a 4D spatiotemporal representation for ambient assisted living

This paper presents a detailed analysis of a 4D representation of events, which are generated by a dynamic stereo vision sensor for the recognition of person's fall. Dynamic vision detectors consist of self-signaling pixels that autonomously react to scene dynamics and asynchronously generate events upon relative light intensity change. Their complete on-chip redundancy reduction, wide dynamic range and high temporal resolution allow efficient and continuous activity monitoring in natural environment. Using a stereo pair of dynamic vision detectors, it is possible to represent the scene dynamics in a 4D space (including time) at a high temporal resolution. In this work, we performed 100 recordings of scenarios including falls in indoor environment using this dynamic stereo vision sensor. Seven features have been extracted and analyzed for three types of falls such that robust parameters will be kept for fall recognition. The result of this analysis is shown in this work with promising outcomes.
Spatial and Spectral Based Segmentation of Text in Multispectral Images of Ancient Documents

In this paper we propose a character segmentation method for multispectral images of ancient documents. Due to the low quality of the images the main idea of this study is to combine the multispectral behavior and contextual spatial information. Therefore we utilize a Markov Random Field model using the spectral information of the images and stroke properties to include spatial dependencies of the characters. Since the stroke properties and the Gaussian parameters for the imaging model are evaluated automatically the proposed segmentation method requires no training phase. We compared the method to state of the art character segmentation methods and demonstrate the effectiveness of combining spectral and spatial features for the segmentation of characters in multispectral images.
A Robust SIFT Descriptor for Multispectral Images

This letter presents a novel method for the description of multispectral image keypoints. The method proposed is based on a modified SIFT algorithm. It uses normalized gradients as local image features for the description of keypoints in order to achieve robustness against non linear intensity changes between multispectral images. The experimental results show that the method proposed achieves a better matching performance and outperforms the SIFT algorithm.
ICFHR 2014 Competition on Handwritten Digit String Recognition in Challenging Datasets (HDSRC 2014)

This paper presents the results of the HDSRC 2014 competition on handwritten digit string recognition in challenging datasets organized in conjunction with ICFHR 2014. The general objective of this competition is to identify, evaluate and compare recent developments in Western Arabic digit string recognition with varying length. In addition, this competition introduces two new challenging datasets for benchmarking. We describe competition details including the datasets and evaluation measures used, and give a comparative performance analysis of six (6) participating methods along with a short description of the respective methodologies.
Next view planning for a combination of passive and active acquisition techniques

In order to create a complete three-dimensional model of an object based on its two-dimensional images, the images have to be acquired from different views. An increasing number of views generally improves the accuracy of the final 3D model but it also increases the time needed to build the model. The number of the possible views can theoretically be infinite. Therefore, it makes sense to try to reduce the number of views to a minimum while preserving a certain accuracy of the model, especially in applications for which the performance is an important issue. We show an approach to next view planning for a fusion of shape from Silhouette, as an example of a passive 3D reconstruction technique, and shape from structured light, as an example of an active 3D reconstruction technique in order to get 3D shape reconstruction with minimal different views. Results of the algorithm developed are presented for both synthetic and real input images.
Strip shredded document reconstruction using optical character recognition

Document reconstruction affects different areas such as archeology, philology and forensics. A reconstruction of fragmented writing materials allows to retrieve and to analyze the lost content. Due to the complexity of reconstruction, automated algorithms are necessary. A reconstruction methodology for shredded documents is presented in this paper which recognizes characters at the stripes' borders and matches them subsequently. In order to achieve this, an Optical Character Recognition (OCR) system is exploited, that is capable of recognizing partially visible characters by means of local features. Thus, no binarization needs to be performed. Preliminary results show the ability of matching shredded documents using the information of cut characters. (6 pages)
Line-based landmark recognition for self-localization of soccer robots

This paperl describes visual landmark extraction for self localization of an auitonomolus mobile robot in at wellknown dynamic environment. The gradient based Hough transform provides the strongest groupings ofcollineair pixels having roughly the same edge orientcation. Grouips of pixels are then processed to calcuilate length and end points of line segments, which together with the length anid direction of the normcal completes the description of the line segmnents. This is followed by classification offield markinigs which are bright lines and aircs on a dark background forming double edges of opposite gradient. Corners, junctions and line intersections are determined by the interpretation of detected line segments. Simulation results illustrate the performance of the method u(sing both real and sYnthetic
Investigation on traditional and modern ceramic documentation

Archaeology is at a point where it can benefit greatly from the application of computer vision methods, and in turn provides a large number of new, challenging and interesting conceptual problems and data for computer science. This is true in particular in the study of ceramics - the most abundant and widespread of all archaeological finds. The traditional way of documenting archaeological sherds is to draw the profile line, which is the intersection of a sherd along the axis of symmetry. A profilograph is a mechanical device, which can directly acquire and transfer a profile line by pin-pointing the profile on a sherd to a computer. We developed a fully automated vision system, which is able to compute the profile line out of the acquired 3D model of the fragment. In this paper we want to give a thorough comparison between the traditional manual approach, the profilograph and our system and present an improvement of the robustness of our approach by finding circular rills on the fragments. Practical experiments have been undertaken at the excavation Tel Dor in Israel.
Text Classification and Document Layout Analysis of Paper Fragments

In general document image analysis methods are pre-processing steps for Optical Character Recognition (OCR) systems. In contrast, the proposed method aims at clustering document snippets, so that an automated clustering of documents can be performed. Therefore, words are classified according to printed text, manuscripts, and noise. Where, the third class corrects falsely segmented background elements. Having classified text elements, a layout analysis is carried out which groups words into text lines and paragraphs. A back propagation of the class weights - assigned to each word in the first step - enables correcting wrong class labels. The proposed method shows promising results on a dataset consisting of document snippets with varying shapes, content writing and layout. In addition, the system is compared to page segmentation methods of the ICDAR 2009 Page Segmentation Competition.
Profile-based Pottery Reconstruction

A major obstacle to the broader use of 3D object reconstruction and modeling is the extent of manual intervention needed. Such interventions are currently extensive and exist throughout every phase of a 3D reconstruction project: collection of images, image management, establishment of sensor position and image orientation, extracting the geometric information describing an object, and merging geometric, texture and semantic data. We present a fully automated approach to pottery reconstruction based on the fragment profile, which is the cross-section of the fragment in the direction of the rotational axis of symmetry. We demonstrate the method and give results on synthetic and real data.
Retrieval of striated toolmarks using convolutional neural networks

The authors propose TripNet as method for calculating similarities between striated toolmark images. The objective for this system is detecting and comparing characteristics of the tools while being invariant to varying parameters like angle of attack, substrate material, and lighting conditions. Instead of designing a handcrafted feature extractor customised for this task, the authors propose the use of a convolutional neural network. With the proposed system, one-dimensional profiles extracted from images of striated toolmarks are mapped into an embedding. The system is trained by minimising a triplet loss function, so that a similarity measure is defined by the L 2 distance in this embedding. The performance is evaluated on the NFI Toolmark database containing 300 striated toolmarks of screwdrivers published by the Netherlands Forensic Institute. The system proposed is able to adapt to a large range of angles of attack, achieving a mean average precision of 0.95 for toolmark comparisons with differences in angle of attack of 15 ∘ – 45 ∘ . Furthermore, four different triplet selection approaches are proposed and their effect on the retrieval of toolmarks from a database of unseen tools is evaluated in detail.
Binarization-Free Text Line Segmentation for Historical Documents Based on Interest Point Clustering

Segmenting page images into text lines is a crucial pre-processing step for automated reading of historical documents. Challenging issues in this open research field are given \eg by paper or parchment background noise, ink bleed-through, artifacts due to aging, stains, and touching text lines. In this paper, we present a novel binarization-free line segmentation method that is robust to noise and copes with overlapping and touching text lines. First, interest points representing parts of characters are extracted from gray-scale images. Next, word clusters are identified in high-density regions and touching components such as ascenders and descenders are separated using seam carving. Finally, text lines are generated by concatenating neighboring word clusters, where neighborhood is defined by the prevailing orientation of the words in the document. An experimental evaluation on the Latin manuscript images of the Saint Gall database shows promising results for real-world applications in terms of both accuracy and efficiency.
ICDAR 2013 Competition on Handwritten Digit Recognition (HDRC 2013)

This paper presents the results of the HDRC 2013 competition for recognition of handwritten digits organized in conjunction with ICDAR 2013. The general objective of this competition is to identify, evaluate and compare recent developments in character recognition and to introduce a new challenging dataset for benchmarking. We describe competition details including dataset and evaluation measures used, and give a comparative performance analysis of the nine (9) submitted methods along with a short description of the respective methodologies.
MSIO: MultiSpectral Document Image BinarizatIOn

MultiSpectral (MS) imaging enriches document digitization by increasing the spectral resolution. We present a methodology which detects a target ink in document images by taking into account this additional information. The proposed method performs a rough foreground estimation to localize possible ink regions. Then, the Adaptive Coherence Estimator (ACE), a target detection algorithm, transforms the MS input space into a single gray-scale image where values close to one indicate ink. A spatial segmentation using GrabCut on the target detection's output is computed to create the final binary image. To find a baseline performance, the method is evaluated on the three most recent Document Image Binarization COntests (DIBCO) despite the fact that they only provide RGB images. In addition, an evaluation on three publicly available MS datasets is carried out. The presented methodology achieved the highest performance at the MultiSpectral Text Extraction (MS-TEx) contest 2015.
CVL-DataBase: An Off-Line Database for Writer Retrieval, Writer Identification and Word Spotting

In this paper a public database for writer retrieval, writer identification and word spotting is presented. The CVL-Database consists of 7 different handwritten texts (1 German and 6 English Texts) and 311 different writers. For each text an RGB color image (300 dpi) comprising the handwritten text and the printed text sample are available as well as a cropped version (only handwritten). A unique ID identifies the writer, whereas the bounding boxes for each single word are stored in an XML file. An evaluation of the best algorithms of the ICDAR and ICHFR writer identification contest has been performed on the CVL-database.
3D modeling of archaeological vessels using shape from silhouette

An algorithm for the automatic construction of a 3D model of archaeological vessels 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 shape of objects with handles in 3d 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. The object's silhouette is the only feature which is extracted from an input image. Images are acquired by rotating the object on a turntable in front of a stationary camera. The algorithm uses an octree representation of the model, and builds this model incrementally, by performing limited processing of all input images for each level of the octree. Beginning from the root node at the level 0 a rough model of the object is obtained quickly and is refined as the processed level of the octree increases. Results of the algorithm developed are presented for both synthetic and real input images.
Texture and Profile Features for Drawing Media Recognition in Underdrawings

In this study we analyze texture and profile features of painted strokes in order to identify the drawing media used for sketching underdrawings. Underdrawings are preliminary drawings on the panel prepared for paintings and are unseen in the finished work. Cameras working in the near infrared range allow the visualization of underdrawings. Due to the tiny width of the strokes we perform an alignment of the feature extraction windows in order to obtain a major content of the stroke texture. The method is tested on strokes applied on test panels and underdrawing strokes in IR images of medieval paintings
Automated segmentation of archaeological profiles for classification

Classification and reconstruction of archaeological fragments is based on the profile, which is the cross-section of the fragment in the direction of the rotational axis of symmetry. In order to segment the profile into primitives like rim, wall, and base, rules based on expert knowledge are created. The input data for the estimation of the profile is a set of points produced by the acquisition system. A function fitting this set is constructed and later on processed to find the characteristic points necessary to classify the original fragment. The one we propose is based on B-splines or bell-shaped splines.
Embedded Real-Time Ball Detection Unit for the YABIRO Biped Robot

Estimation of objects in a 3D space is a fundamental problem in computer vision and robotics. This paper describes an algorithm and its implementation for a vision module as a sensor of a biped robot (YABIRO). The embedded vision sensor is able to estimate the position of objects like spheres in 3D space. Objects are defined with their size and color in a model. The vision sensor detects the positions or at least the directions to the objects and stores them in a history. The algorithm includes a new voting system for detected objects, based on how trustable the detection was, and a new edge filter to terminate edges on the circle border for the circle detection. The systems frame rate depends on the area of interest and lies between 5 Hz and 20 Hz. With a mechanical size of 36times32mm it is smaller than a matchbox