Fabian Hollaus

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.

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.

Multispectral image acquisition of ancient manuscripts

This paper presents image acquisition and readability enhancement techniques based on multispectral imaging. In an interdisciplinary manuscript and palimpsest research project an imaging system using a combination of LED illumination and spectral filtering was developed. On basis of the resulting multispectral image information the readability of the texts is enhanced and palimpsest texts are made visible by applying two different methods of Blind Source Separation, namely Principal Component Analysis and Independent Component Analysis.

Binarization of MultiSpectral Document Images

This work is concerned with the binarization of document images caputured by MultiSpectral Imaging MSI systems. The documents imaged are historical manuscripts and MSI is used to gather more information compared to traditional RGB photographs or scans. The binarization method proposed makes use of a state-of-the-art binarization algorithm, which is applied on a single image taken from the stack of multispectral images. This output is then combined with the output of a target detection algorithm. The target detection method is named Adaptive Coherence Estimator ACE and it is used to improve the binarization performance. Numerical results show that the combination of both algorithms leads to a performance increase. Additionally, the results exhibit that the method performs partially better than other binarization methods designed for grayscale and multispectral images.

Recognition of degraded ancient characters based on dense SIFT

This paper presents a novel method for the recognition of ancient characters in historical documents. The method proposed is especially designed for degraded documents in which the character recognition based on state of the art methods is hard to achieve due to faded out ink, stain and background noise. The method proposed deals with such degradations by making use of the Dense SIFT features and the nearest neighbor distance maps. The maps encode the distances between the features of the documents and the training set. This results in local minima in the nearest neighbor distance maps which help in localization and recognition of characters in the documents. The experiments on three datasets show that the method proposed achieves a better character recognition performance compared to another method designed for similar historical documents.

Writer identification on historical Glagolitic documents

This work aims at automatically identifying scribes of historical Slavonic manuscripts. The quality of the ancient documents is partially degraded by faded-out ink or varying background. The writer identification method used is based on image features, which are described with Scale Invariant Feature Transform (SIFT) features. A visual vocabulary is used for the description of handwriting characteristics, whereby the features are clustered using a Gaussian Mixture Model and employing the Fisher kernel. The writer identification approach is originally designed for grayscale images of modern handwritings. But contrary to modern documents, the historical manuscripts are partially corrupted by background clutter and water stains. As a result, SIFT features are also found on the background. Since the method shows also good results on binarized images of modern handwritings, the approach was additionally applied on binarized images of the ancient writings. Experiments show that this preprocessing step leads to a significant performance increase: The identification rate on binarized images is 98.9%, compared to an identification rate of 87.6% gained on grayscale images.

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.

Mass Digitization of Archival Documents using Mobile Phones

Digital copies of historical documents are needed for the Digital Humanities. Currently, cameras of standard mobile phones are able to capture documents with a resolution of about 330 dpi for document sizes up to DIN A4 (German standard, 297 x 210 mm), which allows a digitization of documents using a standard device. Thus, scholars are able to take images of documents in archives themselves without the need of book scanners or other devices. This paper presents a scanning app, which comprises a real time page detection, quality assessment (focus measure) and an automated detection of a page turn over if books are scanned. Additionally, a portable device - the ScanTent - to place the mobile phone during scanning is presented. The page detection is evaluated on the ICDAR2015 SmartDoc competition dataset and shows a reliable page detection with an average Jaccard index of 75%.

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 detections 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.

Investigation of Ancient Manuscripts based on Multispectral Imaging

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.