Vassilis Papavassiliou

Handwritten document image segmentation into text lines and words

Two novel approaches to extract text lines and words from handwritten document are presented. The line segmentation algorithm is based on locating the optimal succession of text and gap areas within vertical zones by applying Viterbi algorithm. Then, a text-line separator drawing technique is applied and finally the connected components are assigned to text lines. Word segmentation is based on a gap metric that exploits the objective function of a soft-margin linear SVM that separates successive connected components. The algorithms tested on the benchmarking datasets of ICDAR07 handwriting segmentation contest and outperformed the participating algorithms.

Robust text-line and word segmentation for handwritten documents images

This paper addresses the problem of automatic text-line and word segmentation in handwritten document images. Two novel approaches are presented, one for each task. In text-line segmentation a Viterbi algorithm is proposed while an SVM-based metric is adopted to locate words in each text-line. The overall algorithm was tested in the ICDAR2007 handwriting segmentation contest and showed highly promising results.

GRPOLY-DB: An old Greek polytonic document image database

Recognition of old Greek document images containing polytonic (multi accent) characters is a challenging task due to the large number of existing character classes (more than 270) which cannot be handled sufficiently by current OCR technologies. Taking into account that the Greek polytonic system was used from the late antiquity until recently, a large amount of scanned Greek documents still remains without full test search capabilities. In order to assist the progress of relevant research, this paper introduces the first publicly available old Greek polytonic database GRPOLY-DB for the evaluation of several document image processing tasks. It contains both machine-printed and handwritten documents as well as annotation with ground-truth information that can be used for training and evaluation of the most commou document image processing tasks, i.e.. text line and word segmentation, test recognition, isolated character recognition and word spotting. Results using several representative baseline technologies are also presented in order to help researchers evaluate their methods and advance the frontiers of old Greek document image recognition and word spotting.

A Morphological Approach for Text-Line Segmentation in Handwritten Documents

Document image segmentation to text lines is a critical stage towards unconstrained handwritten document recognition. Although morphological operations proved to be effective in processing machine-printed documents for several issues, similar methods for unconstraint-handwritten documents lack accuracy. We propose an efficient method based on binary morphology for text-line segmentation in such documents. The basic steps of our approach are: a) sub sampling and binary rank order filtering to enhance the text-line structures and b) applying dilations and (p,q)-th generalized foreground rank openings successively to join close and horizontally overlapping regions while preventing a merge in the vertical direction. The method tested on the benchmarking dataset of the ICDAR07 handwriting segmentation contest and show remarkable results.

A Morphology Based Approach for Binarization of Handwritten Documents

Document image binarization is an initial though critical stage towards the recognition of the text components of a document. This paper describes an efficient method based on mathematical morphology for extracting text regions from degraded handwritten document images. The basic stages of our approach are: (a) top-hat-by-reconstruction to produce a filtered image with reasonable even background, (b) region growing starting from a set of seed points and attaching to each seed similar intensity neighboring pixels and (c) conditional extension of the initially detected text regions based on the values of the second derivative of the filtered image. The method was evaluated on the benchmarking dataset of the International Document Image Binarization Contest (DIBCO 2011) and show promising results.

A System for Recognition of On-Line Handwritten Mathematical Expressions

We present a system for recognizing online mathematical expressions (ME). Symbol recognition is based on a template elastic matching distance between pen direction features. The structural analysis of the ME is based on extracting the baseline of the ME and then classifying symbols into levels above and below the baseline. The symbols are then sequentially analyzed using six spatial relations and a respective 2d structure is processed to give the resulting MathML representation of the ME. The system was evaluated on the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2011 datasets and demonstrates promising results.

Enhancing Handwritten Word Segmentation by Employing Local Spatial Features

This paper proposes an enhancement of our previously presented word segmentation method (ILSPLWseg) [1] by exploiting local spatial features. ILSP-LWseg is based on a gap metric that exploits the objective function of a soft-margin linear SVM that separates successive connected components (CCs). Then a global threshold for the gap metrics is estimated and used to classify the candidate gaps in within or between words classes. In the proposed enhancement the initial categorization is examined against the local features (i.e. margin and slope of the linear classifier for every pair of CCs in each text line) and a refined classification is applied for each text line. The method was tested on the benchmarking datasets of ICDAR07, ICDAR09 and ICFHR10 handwriting segmentation contests and performs better than the winning algorithm.

Recognition of spatial relations in mathematical formulas

A critical issue in recognition of mathematical expressions is the identification of the spatial relations of the symbols or/and sub-expressions that comprise the entire mathematical formula. This paper addresses the problem of structural analysis of mathematical expressions by constructing appropriate feature vectors to represent the spatial affinity of the objects (mathematical symbols or sub-expressions) under examination and employing two popular machine learning techniques: (i) Support Vector Machines (SVM) and (ii) Artificial Neural Networks (ANN) to recognize the spatial relation between these objects. In order to evaluate the proposed techniques, we use Math Brush, a large publicly available dataset of mathematical expressions with annotated spatial relations, and a subset of spatial relations derived from the mathematical expressions the CROHME 2012 dataset. The experimental results give an overall mean error rate of 2.8% for the SVM and 3.4% for the ANN classifiers respectively, which are at par with other approaches evaluated on the same datasets.

Structural analysis of online handwritten mathematical symbols based on support vector machines

Mathematical expression recognition is still a very challenging task for the research community mainly because of the two-dimensional (2d) structure of mathematical expressions (MEs). In this paper, we present a novel approach for the structural analysis between two on-line handwritten mathematical symbols of a ME, based on spatial features of the symbols. We introduce six features to represent the spatial affinity of the symbols and compare two multi-class classification methods that employ support vector machines (SVMs): one based on the “one-against-one” technique and one based on the “one-against-all”, in identifying the relation between a pair of symbols (i.e. subscript, numerator, etc). A dataset containing 1906 spatial relations derived from the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2012 training dataset is constructed to evaluate the classifiers and compare them with the rule-based classifier of the ILSP-1 system participated in the contest. The experimental results give an overall mean error rate of 2.61% for the “one-against-one” SVM approach, 6.57% for the “one-against-all” SVM technique and 12.31% error rate for the ILSP-1 classifier.

Recognition of historical Greek polytonic scripts using LSTM networks

This paper reports on high-performance Optical Character Recognition (OCR) experiments using Long Short-Term Memory (LSTM) Networks for Greek polytonic script. Even though there are many Greek polytonic manuscripts, the digitization of such documents has not been widely applied, and very limited work has been done on the recognition of such scripts. We have collected a large number of diverse document pages of Greek polytonic scripts in a novel database, called Polyton-DB, containing 15; 689 textlines of synthetic and authentic printed scripts and performed baseline experiments using LSTM Networks. Evaluation results show that the character error rate obtained with LSTM varies from 5.51% to 14.68% (depending on the document) and is better than two well-known OCR engines, namely, Tesseract and ABBYY FineReader.