Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclera-based biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias.

The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting.

DIBCO 2019 is the international Competition on Document Image Binarization organized in conjunction with the ICDAR 2019 conference. The general objective of the contest is to identify current advances in document image binarization of machine-printed and handwritten document images using performance evaluation measures that are motivated by document image analysis and recognition requirements. This paper describes the competition details including the evaluation measures used as well as the performance of the 24 submitted methods along with a brief description of each method.

H-DIBCO 2018 is the international Handwritten Document Image Binarization Contest organized in the context of ICFHR 2018 conference. The general objective of the contest is to record recent advances in document image binarization using established evaluation performance measures. This paper describes the contest details including the evaluation measures used as well as the performance of the 8 submitted methods along with a brief description of each method.

Performance evaluation is one of the main research topics in information retrieval. Evaluation metrics are used to quantify various performance aspects of a retrieval method. These metrics assist in identifying the optimum method for a specific retrieval challenge but also to allow its parameters fine-tuning in order to achieve a robust operation for a given set of requirements specification. In this work, we present RETRIEVAL, a Web-based integrated information retrieval performance evaluation platform. It offers a number of metrics that are popular within the scientific community, so as to compose an efficient framework for implementing performance evaluation. We discuss the functionality of RETRIEVAL by citing important aspects such as the data input approaches, the user-level performance metrics parameterization, the evaluation scenarios, the interactive plots, and the performance reports repository that offers both archiving and download functionalities.

DIBCO 2017 is the international Competition on Document Image Binarization organized in conjunction with the ICDAR 2017 conference. The general objective of the contest is to identify current advances in document image binarization of machine-printed and handwritten document images using performance evaluation measures that are motivated by document image analysis and recognition requirements. This paper describes the competition details including the evaluation measures used as well as the performance of the 26 submitted methods along with a brief description of each method.

An important step for the document analysis and recognition pipeline is the document image binarization procedure. In the case of historical handwritten document images, the inherent degradation of the documents requires a preprocessing step aiming to enhance the image and improve the subsequent binarization step. To address this challenge a new document image enhancement method is proposed based on Bio-Inspired Models and especially on the OFF-center ganglion cells of the Human Vision System. Experimental results demonstrate the improvement of standard binarization methods when the proposed enhancement method is used.

A fully automatic mesh segmentation scheme using heterogeneous graphs is presented. We introduce a spectral framework where local geometry affinities are coupled with surface patch affinities. A heterogeneous graph is constructed combining two distinct graphs: a weighted graph based on adjacency of patches of an initial over-segmentation, and the weighted dual mesh graph. The partitioning relies on processing each eigenvector of the heterogeneous graph Laplacian individually, taking into account the nodal set and nodal domain theory. Experiments on standard datasets show that the proposed unsupervised approach outperforms the state-of-the-art unsupervised methodologies and is comparable to the best supervised approaches.

Word spotting strategies employed in historical handwritten documents face many challenges due to variation in the writing style and intense degradation. In this paper, a new method that permits effective word spotting in handwritten documents is presented that it relies upon document-oriented local features, which take into account information around representative keypoints as well a matching process that incorporates spatial context in a local proximity search without using any training data. Experimental results on four historical handwritten data sets for two different scenarios (segmentation-based and segmentation-free) using standard evaluation measures show the improved performance achieved by the proposed methodology.

H-DIBCO 2016 is the international Handwritten Document Image Binarization Contest organized in the context of ICFHR 2016 conference. The general objective of the contest is to identify current advances in document image binarization of handwritten document images using performance evaluation measures that are motivated by document image analysis and recognition requirements. This paper describes the contest details including the evaluation measures used as well as the performance of the 12 submitted methods along with a brief description of each method.

The H-KWS 2016, organized in the context of the ICFHR 2016 conference aims at setting up an evaluation framework for benchmarking handwritten keyword spotting (KWS) examining both the Query by Example (QbE) and the Query by String (QbS) approaches. Both KWS approaches were hosted into two different tracks, which in turn were split into two distinct challenges, namely, a segmentation-based and a segmentation-free to accommodate different perspectives adopted by researchers in the KWS field. In addition, the competition aims to evaluate the submitted training-based methods under different amounts of training data. Four participants submitted at least one solution to one of the challenges, according to the capabilities and/or restrictions of their systems. The data used in the competition consisted of historical German and English documents with their own characteristics and complexities. This paper presents the details of the competition, including the data, evaluation metrics and results of the best run of each participating methods.

Document image binarization is an important step in the document image analysis and recognition pipeline. H-DIBCO 2014 is the International Document Image Binarization Competition which is dedicated to handwritten document images organized in conjunction with ICFHR 2014 conference. The objective of the contest is to identify current advances in handwritten document image binarization using meaningful evaluation performance measures. This paper reports on the contest details including the evaluation measures used as well as the performance of the 7 submitted methods along with a short description of each method.

H-KWS 2014 is the Handwritten Keyword Spotting Competition organized in conjunction with ICFHR 2014 conference. The main objective of the competition is to record current advances in keyword spotting algorithms using established performance evaluation measures frequently encountered in the information retrieval literature. The competition comprises two distinct tracks, namely, a segmentation-based and a segmentation-free track. Five (5) distinct research groups have participated in the competition with three (3) methods for the segmentation-based track and four (4) methods for the segmentation-free track. The benchmarking datasets that were used in the contest contain both historical and modern documents from multiple writers. In this paper, the contest details are reported including the evaluation measures and the performance of the submitted methods along with a short description of each method.

Many word spotting strategies for the modern documents are not directly applicable to historical handwritten documents due to writing styles variety and intense degradation. In this paper, a new method that permits effective word spotting in handwritten documents is presented that relies upon document-specific local features which take into account texture information around representative key points. Experimental work on two historical handwritten datasets using standard evaluation measures shows the improved performance achieved by the proposed methodology.

DIBCO 2013 is the international Document Image Binarization Contest organized in the context of ICDAR 2013 conference. The general objective of the contest is to identify current advances in document image binarization for both machine-printed and handwritten document images using evaluation performance measures that conform to document image analysis and recognition. This paper describes the contest details including the evaluation measures used as well as the performance of the 23 submitted methods along with a short description of each method.

Textual information in images constitutes a very rich source of high-level semantics for multimedia indexing and retrieval. In this paper, a new approach is proposed for detecting text in natural images inspired by the properties of the Human Visual System (HVS). In particular, the detection is based upon the functionality of the OFF and ON center-surround cells of the HVS. Cells are combined at different scales to guide a both efficient and effective algorithm. Performance evaluation relies on the ICDAR 2011 Robust Reading Dataset.

Document image binarization is of great importance in the document image analysis and recognition pipeline since it affects further stages of the recognition process. The evaluation of a binarization method aids in studying its algorithmic behavior, as well as verifying its effectiveness, by providing qualitative and quantitative indication of its performance. This paper addresses a pixel-based binarization evaluation methodology for historical handwritten/machine-printed document images. In the proposed evaluation scheme, the recall and precision evaluation measures are properly modified using a weighting scheme that diminishes any potential evaluation bias. Additional performance metrics of the proposed evaluation scheme consist of the percentage rates of broken and missed text, false alarms, background noise, character enlargement, and merging. Several experiments conducted in comparison with other pixel-based evaluation measures demonstrate the validity of the proposed evaluation scheme.

H-DIBCO 2012 is the International Document Image Binarization Competition which is dedicated to handwritten document images organized in conjunction with ICFHR 2012 conference. The objective of the contest is to identify current advances in handwritten document image binarization using meaningful evaluation performance measures. This paper reports on the contest details including the evaluation measures used as well as the performance of the 24 submitted methods along with a short description of each method.

In a number of types of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may be present in the same document image, giving rise to significant issues within a digitisation and recognition pipeline. It is therefore necessary to separate the two types of text before applying different recognition methodologies to each. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of Visual Words paradigm (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten, Machine Printed or Noise is made by a Support Vector Machine classifier. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with a new dataset.

In this paper we present a binarization technique for textual content in video frames which can be applied in the resulting image of the text detection step aiming in an improved OCR performance. The proposed technique is based on the detection of the text baselines in order to define the main body of the text. The main body of the text is used to detect the stroke width of the characters which will address the two consecutive locally adaptive binarization steps that follow. At the first step, we use different valuation in parameters for the inside and outside area of the main body of the text. To include the thinned or broken binarized parts that may exist outside the main text body, convex hull analysis is performed so that the entire text body is considered. At the second step, binarization is performed with different valuation in parameters for the inside and outside area of the entire text body. The effectiveness of the proposed technique is demonstrated by both qualitative and OCR-based evaluation.

DIBCO 2011 is the International Document Image Binarization Contest organized in the context of ICDAR 2011 conference. The general objective of the contest is to identify current advances in document image binarization for both machine-printed and handwritten document images using evaluation performance measures that conform to document image analysis and recognition. This paper describes the contest details including the evaluation measures used as well as the performance of the 18 submitted methods along with a short description of each method.

Document digitization with either flatbed scanners or camera-based systems results in document images which often suffer from warping and perspective distortions that deteriorate the performance of current OCR approaches. In this paper, we present a goal-oriented rectification methodology to compensate for undesirable document image distortions aiming to improve the OCR result. Our approach relies upon a coarse-to-fine strategy. First, a coarse rectification is accomplished with the aid of a computationally low cost transformation which addresses the projection of a curved surface to a 2-D rectangular area. The projection of the curved surface on the plane is guided only by the textual content's appearance in the document image while incorporating a transformation which does not depend on specific model primitives or camera setup parameters. Second, pose normalization is applied on the word level aiming to restore all the local distortions of the document image. Experimental results on various document images with a variety of distortions demonstrate the robustness and effectiveness of the proposed rectification methodology using a consistent evaluation methodology that encounters OCR accuracy and a newly introduced measure using a semi-automatic procedure.

H-DIBCO 2010 is the International Document Image Binarization Contest which is dedicated to handwritten document images organized in conjunction with ICFHR 2010 conference. The general objective of the contest is to identify current advances in handwritten document image binarization using meaningful evaluation performance measures. This paper reports on the contest details including the evaluation measures used as well as the performance of the 17 submitted methods along with a short description of each method.

DIBCO 2009 is the first International Document Image Binarization Contest organized in the context of ICDAR 2009 conference. The general objective of the contest is to identify current advances in document image binarization using established evaluation performance measures. This paper describes the contest details including the evaluation measures used as well as the performance of the 43 submitted methods along with a short description of each method.

In this paper, a new efficient word spotting methodology is presented that can be applied to historical printed documents without requiring any previous block or word segmentation step. Our aim is to address a methodology which is segmentation-free since in many cases of historical documents, the segmentation process does not produce meaningful results due to unconstraint layout, several degradations or typesetting imperfections. The proposed method is based on block-based document image descriptors that are used at a template matching process satisfying invariance in terms of translation, rotation and scaling. Improvement in terms of time expense is obtained by applying the matching process only on salient regions of the image. Experimental results on a database with representative historical printed documents prove the efficiency of the proposed approach.

In this paper, a new document image binarization technique is presented, as an improved version of the state-of-the-art adaptive logical level technique (ALLT). The original ALLT depends on fixed windows to extract essential features such as the character stroke width. Since characters with several different stroke widths may exist within a region, this can lead to erroneous results. In our approach, we use local adaptive binarization as a guide to our adaptive stroke width detection. The skeleton and the contour points of the binarization output are combined to identify locally the stroke width. Additionally, we introduce an adaptive local parameter ldquobetardquo that enhances the characters and improves the overall performance. In this way, we achieve more accurate binarization results in both handwritten and printed documents with a particular focus on degraded historical documents. Experimental results prove the effectiveness of the proposed technique compared to other state-of-the-art methodologies.

One of the major challenges in camera document analysis is to deal with the page curl and perspective distortions. In spite of the prevalence of dewarping techniques, no standard for their performance evaluation method exists with most of the evaluation done to concentrate in visual pleasing impressions. This paper presents an objective evaluation methodology for document image dewarping techniques. First, manually selected sets of points of the initial warped image are matched with the corresponding points of the dewarping result using the scale invariant feature transform (SIFT). Each set corresponds to a representative text line of the image. Then, based on cubic polynomial curves that fit to the selected text lines, a comprehensive measure which reflects the entire performance of a dewarping technique in a concise quantitative manner is calculated. Experiments applying the proposed performance evaluation methodology on two state of the art dewarping techniques as well as a commercial package are presented.

This paper proposes a hybrid system for text detection in video frames. The system consists of two main stages. In the first stage text regions are detected based on the edge map of the image leading in a high recall rate with minimum computation requirements. In the sequel, a refinement stage uses an SVM classifier trained on features obtained by a new local binary pattern based operator which results in diminishing false alarms. Experimental results show the overall performance of the system that proves the discriminating ability of the proposed feature set.

This paper presents a new adaptive approach for the binarization and enhancement of historical and degraded documents. The proposed method is based on (i) efficient pre-processing; (ii) the combination of the results of several state-of-the-art binarization methodologies; (iii) the incorporation of edge information and (iv) the application of efficient image post-processing based on mathematical morphology for the enhancement of the final result. The proposed method demonstrated superior performance against six well-known techniques on numerous historical handwritten and machine-printed documents mainly from the Library of Congress of the United States archive. The performance evaluation was based on a consistent and concrete methodology.

Evaluation of document image binarization techniques is a tedious task that is mainly performedby a human expert or by involving an OCR engine. This paper presents an objective evaluation methodology for document image binarization techniques that aims to reduce the human involvement in the ground truth construction and consecutive testing. A skeletonized ground truth image is produced by the user following a semi-automatic procedure. The estimated ground truth image can aid in evaluating the binarization result in terms of recall and precision as well as to further analyze the result by calculating broken and missing text, deformations and false alarms. A detailed description of the methodology along with a benchmarking of the six (6) most promising state-of-the-art binarization algorithms based on the proposed methodology is presented.

Dewarping of camera document images has attracted a lot of interest over the last few years since warping not only reduces the document readability but also affects the accuracy of an OCR application. In this paper, a two-step approach for efficient dewarping of camera document images is presented. At a first step, a coarse dewarping is accomplished with the help of a transformation model which maps the projection of a curved surface to a 2D rectangular area. The projection of the curved surface is delimited by the two curved lines which fit the top and bottom text lines along with the two straight lines which fit to the left and right text boundaries. At a second step, fine dewarping is achieved based on words detection. All words are pose normalized guided by the lower and upper word baselines. Experimental results on several camera document images demonstrate the robustness and effectiveness of the proposed technique.

This paper presents a new adaptive approach for document image binarization. The proposed method is mainly based on the combination of several state- of-the-art binarization methodologies as well as on the efficient incorporation of the edge information of the gray scale source image. An enhancement step based on mathematical morphology operations is also involved in order to produce a high quality result while preserving stroke information. The proposed method demonstrated superior performance against six (6) well-known techniques on numerous degraded handwritten and machine- printed documents. The performance evaluation is based on visual criteria as well as on an objective evaluation methodology.

In this paper, we present an overview of the 3D object retrieval method that we employed in our participation to the generic models track of SHREC 2008 organized by the AIM@SHAPE network of excellence. The proposed methodology is detailed in [2]. Our method is based on a hybrid scheme where 2D features as well as 3D features are extracted from a 3D model which has been previously normalized for rotation using two alternative alignment techniques. The alignment methods that are used are CPCA and NPCA. The 2D features are Fourier coefficients extracted from a set of depth buffers and the 3D features are spherical harmonic coefficients extracted from a spherical function-based representation of a 3D model.

Non-linear warping appears in document images when captured by a digital camera or a scanner, especially in the case that these documents are digitized bounded volumes. Arbitrarily warped documents may have several slope changes along the text lines as well as along the words of the same text line. In this paper, a novel segmentation based technique for efficient restoration of arbitrarily warped document images is presented. The proposed technique recovers the documents relying upon (i) text lines and words detection using a novel segmentation technique appropriate for warped documents, (ii) a first draft binary image de-warping based on word rotation and translation according to upper and lower word baselines, and (Hi) a recovery of the original warped image guided by the draft binary image de-warping result. Experimental results on several arbitrarily warped documents prove the effectiveness of the proposed technique.

In this paper, image segmentation is addressed within the framework of nonlinear multiscale watersheds in combination with graph theory. First, a graph is created which decomposes the image in scale and space using the concept of multiscale watersheds. In the subsequent step the obtained graph is partitioned using recursive graph cuts in a coarse to fine manner. In this way, we combine scale and feature measures in a flexible way. The dissimilarity between graph-nodes is estimated by using the Earth Mover's Distance on a featureset that combines color, scale and contrast. Experimental results demonstrate the efficiency of the proposed method for natural scene images.

In this paper, we propose a novel segmentation-free approach for keyword search in historical typewritten documents combining image preprocessing, synthetic data creation, word spotting and user feedback technologies. Our aim is to search for keywords typed by the user in a large collection of digitized typewritten historical documents. The proposed method is based on: (i) image preprocessing for image binarization and enhancement, noisy border and frame removal, orientation and skew correction; (ii) creation of synthetic image words from keywords typed by the user; (Hi) word segmentation using dynamic parameters; (iv) efficient feature extraction for each image word and (v) a retrieval procedure that is optimized by user's feedback. Experimental results prove the efficiency of the proposed approach.

Recognition of handwritten manuscripts is essential for efficient content exploitation of the valuable old Greek historical collections. In this paper, we focus on the problem of recognizing old Greek handwritten manuscripts and propose a novel recognition technique that can be applied to a large number of important historical manuscript collections which are written in lower case letters and originate from St. Catherine's Mount Sinai Monastery. Based on an open and closed cavity character representation, we propose a novel, segmentation-free, fast and efficient technique for the detection and recognition of characters and character ligatures. First, we detect open and closed cavities that exist in the skeletonized character body. Then, the recognition of a specific character or character ligature is based on the protrusible segments that appear in the topological description of the character skeletons. Experimental results prove the efficiency of the proposed approach.

We present a new framework for the hierarchical segmentation of color images. The proposed scheme comprises a nonlinear scale-space with vector-valued gradient watersheds. Our aim is to produce a meaningful hierarchy among the objects in the image using three image components of distinct perceptual significance for a human observer, namely strong edges, smooth segments and detailed segments. The scale-space is based on a vector-valued diffusion that uses the Additive Operator Splitting numerical scheme. Furthermore, we introduce the principle of the dynamics of contours in scale-space that combines scale and contrast information. The performance of the proposed segmentation scheme is presented via experimental results obtained with a wide range of images including natural and artificial scenes.