Baseline detection is a simplified text-line extraction that typically serves as pre-processing for Automated Text Recognition. The cBAD competition benchmarks state-of-the-art baseline detection algorithms. It is the successor of cBAD 2017 with a larger dataset that contains more diverse document pages. The images together with the manually annotated groundtruth are made publicly available which allows other teams to benchmark and compare their methods. We could also evaluate the winning method of cBAD 2017 on the newly introduced dataset which now serves as baseline. This competition shows that the performance of automated baseline detection increased substantially since 2017.

The cTDaR competition aims at benchmarking state-of-the-art table detection (TRACK A) and table recognition (TRACK B) methods. In particular, we wish to investigate and compare general methods that can reliably and robustly identify the table regions within a document image on the one hand, and the table structure on the other hand. Due to the presence of hand-drawn tables and handwritten text, the methods must be robust against various noise conditions, interfering annotations, and variations of the tables. Two new challenging datasets were created to test the behaviour of state-of-the-art table detection and recognition systems on real world data. One dataset consists of modern documents, while the other consists of archival documents with presence of hand-drawn tables and handwritten text. The evaluation scheme is adapted from the ICDAR 2013 Table competition. We received results of Track A from 11 teams and results of Track B from 2 teams. Results for Track A are very good for the top participants. The winner and his runner-up are very close while using very different approaches. Track B was more challenging and only one participant was able to produce good results.

We present in this paper experiments on Table Recognition in hand-written register books. We first explain how the problem of row and column detection is modelled, and then compare two Machine Learning approaches (Conditional Random Field and Graph Convolutional Network) for detecting these table elements. Evaluation was conducted on death records provided by the Archives of the Diocese of Passau. With an F-1 score of 89, both methods provide a quality which allows for Information Extraction. Software and dataset are open source/data.

Text line detection is crucial for any application associated with Automatic Text Recognition or Keyword Spotting. Modern algorithms perform good on well-established datasets since they either comprise clean data or simple/homogeneous page layouts. We have collected and annotated 2036 archival document images from different locations and time periods. The dataset contains varying page layouts and degradations that challenge text line segmentation methods. Well established text line segmentation evaluation schemes such as the Detection Rate or Recognition Accuracy demand for binarized data that is annotated on a pixel level. Producing ground truth by these means is laborious and not needed to determine a method's quality. In this paper we propose a new evaluation scheme that is based on baselines. The proposed scheme has no need for binarization and it can handle skewed as well as rotated text lines. The ICDAR 2017 Competition on Baseline Detection and the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts used this evaluation scheme. Finally, we present results achieved by a recently published text line detection algorithm.

The cBAD competition aims at benchmarking state-of-the-art baseline detection algorithms. It is in line with previous competitions such as the ICDAR 2013 Handwriting Segmentation Contest. A new, challenging, dataset was created to test the behavior of state-of-the-art systems on real world data. Since traditional evaluation schemes are not applicable to the size and modality of this dataset, we present a new one that introduces baselines to measure performance. We received submissions from five different teams for both tracks.

The ICDAR 2017 Competition on Historical Document Writer Identification is dedicated to record the most recent advances made in the field of writer identification. The goal of the writer identification task is the retrieval of pages, which have been written by the same author. The test dataset used in this competition consists of 3600 handwritten pages originating from 13th to 20th century. It contains manuscripts from 720 different writers where each writer contributed five pages. This paper describes the dataset, as well as the details of the competition. Five different institutions submitted six methods which were ranked using identification and retrieval metrics. The paper describes the competition details including the dataset, the evaluation measures used as well as a short description of each submitted method.

This work is concerned with the digitization and analysis of historical documents. The investigation of the documents has been conducted in three successive interdisciplinary projects. The team involved in the projects consists of philologists, chemists and computer scientists specialized in the field of digital image processing. The manuscripts investigated are partially degraded since they have been infected by mold, are corrupted by background clutter or contain faded-out or even erased writings. Since these degradations impede a transcription by scholars and worsen the performance of automated document image analysis techniques, the documents have been imaged with a portable multispectral imaging system. By using this non-invasive investigation technique, the contrast of the faded out characters can be increased, compared to ordinary white light illumination. Post-processing techniques, such as dimension reduction tools, can be used to gain a further legibility increase. The resulting images are used as a basis for further document analysis methods. These methods have been especially designed for the historical documents investigated and involve Optical Character Recognition and writer identification. This paper presents an overview on selected methods that have been developed in the projects.

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.

A ruling classification is presented in this paper. In contrast to state-of-the-art methods which focus on ruling line removal, ruling lines are analyzed for document clustering in the context of document snippet reassembling. First, a background patch is extracted from a snippet at a position which minimizes the inscribed content. A novel Fourier feature is then computed on the image patch. The classification into void, lined and checked is carried out using Support Vector Machines. Finally, an accurate line localization is performed by means of projection profiles and robust line fitting. The ruling classification achieves an F-score of 0.987 evaluated on a dataset comprising real world document snippets. In addition the line removal was evaluated on a synthetically generated dataset where an F-score of 0.931 is achieved. This dataset is made publicly available so as to allow for benchmarking.

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.

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.

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.

Document analysis is done to analyze entire forms (e.g. intelligent form analysis, table detection) or to describe the layout/structure of a document for further processing. A pre-processing step of document analysis methods is a skew estimation of scanned or photographed documents. Current skew estimation methods require the existence of large text areas, are dependent on the text type and can be limited on a specific angle range. The proposed method is gradient based in combination with a Focused Nearest Neighbor Clustering of interest points and has no limitations regarding the detectable angle range. The upside/down decision is based on statistical analysis of ascenders and descenders. It can be applied to entire documents as well as to document fragments containing only a few words. Results show that the proposed skew estimation is comparable with state-of-the-art methods and outperforms them on a real dataset consisting of 658 snippets.

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.

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.

Document analysis is done to analyze entire forms (e.g. intelligent form analysis, table detection) or to describe the layout/structure of a document. In this paper document analysis is applied to snippets of torn documents to calculate features that can be used for reconstruction. The main intention is to handle snippets of varying size and different contents (e.g. handwritten or printed text). Documents can either be destroyed by the intention to make the printed content unavailable (e.g. business crime) or due to time induced degeneration of ancient documents (e.g. bad storage conditions). Current reconstruction methods for manually torn documents deal with the shape, or e.g. inpainting and texture synthesis techniques. In this paper the potential of document analysis techniques of snippets to support a reconstruction algorithm by considering additional features is shown. This implies a rotational analysis, a color analysis, a line detection, a paper type analysis (checked, lined, blank) and a classification of the text (printed or hand written). Preliminary results show that these features can be determined reliably on a real dataset consisting of 690 snippets.

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.

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.