Florian Kleber

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.

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.

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.

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.

Document analysis applied to fragments: feature set for the reconstruction of torn documents

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.

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.

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.

Skew Estimation of Sparsely Inscribed Document Fragments

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.

Document reconstruction by layout analysis of snippets

Document analysis is done to analyze entire forms (e.g. intelligent form analysis, table detection) or to describe the layout/structure of a document. Also skew detection of scanned documents is performed to support OCR algorithms that are sensitive to skew. In this paper document analysis is applied to snippets of torn documents to calculate features for the reconstruction. Documents can either be destroyed by the intention to make the printed content unavailable (e.g. tax fraud investigation, 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, inpainting and texture synthesis techniques. In this paper the possibility of document analysis techniques of snippets to support the matching algorithm by considering additional features are shown. This implies a rotational analysis, a color analysis and a line detection. As a future work it is planned to extend the feature set with the paper type (blank, checked, lined), the type of the writing (handwritten vs. machine printed) and the text layout of a snippet (text size, line spacing). Preliminary results show that these pre-processing steps can be performed reliably on a real dataset consisting of 690 snippets.

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.