Georgios Louloudis

Text line and word segmentation of handwritten documents

In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and words. Text line segmentation is achieved by applying Hough transform on a subset of the document image connected components. A post-processing step includes the correction of possible false alarms, the detection of text lines that Hough transform failed to create and finally the efficient separation of vertically connected characters using a novel method based on skeletonization. Word segmentation is addressed as a two class problem. The distances between adjacent overlapped components in a text line are calculated using the combination of two distance metrics and each of them is categorized either as an inter- or an intra-word distance in a Gaussian mixture modeling framework. The performance of the proposed methodology is based on a consistent and concrete evaluation methodology that uses suitable performance measures in order to compare the text line segmentation and word segmentation results against the corresponding ground truth annotation. The efficiency of the proposed methodology is demonstrated by experimentation conducted on two different datasets: (a) on the test set of the ICDAR2007 handwriting segmentation competition and (b) on a set of historical handwritten documents.

ICDAR 2009 Handwriting Segmentation Contest

This paper presents the results of the Handwriting Segmentation Contest that was organized in the context of the ICDAR2013. The general objective of the contest was to use well established evaluation practices and procedures to record recent advances in off-line handwriting segmentation. Two benchmarking datasets, one for text line and one for word segmentation, were created in order to test and compare all submitted algorithms as well as some state-of-the-art methods for handwritten document image segmentation in realistic circumstances. Handwritten document images were produced by many writers in two Latin based languages (English and Greek) and in one Indian language (Bangla, the second most popular language in India). These images were manually annotated in order to produce the ground truth which corresponds to the correct text line and word segmentation results. The datasets of previously organized contests (ICDAR2007, ICDAR2009 and ICFHR2010 Handwriting Segmentation Contests) along with a dataset of Bangla document images were used as training dataset. Eleven methods are submitted in this competition. A brief description of the submitted algorithms, the evaluation criteria and the segmentation results obtained from the submitted methods are also provided in this manuscript.

Text line detection in handwritten documents

In this paper, we present a new text line detection method for handwritten documents. The proposed technique is based on a strategy that consists of three distinct steps. The first step includes image binarization and enhancement, connected component extraction, partitioning of the connected component domain into three spatial sub-domains and average character height estimation. In the second step, a block-based Hough transform is used for the detection of potential text lines while a third step is used to correct possible splitting, to detect text lines that the previous step did not reveal and, finally, to separate vertically connected characters and assign them to text lines. The performance evaluation of the proposed approach is based on a consistent and concrete evaluation methodology.

ICDAR 2011 Writer Identification Contest

ICDAR 2011 Writer Identification Contest is the first contest which is dedicated to record recent advances in the field of writer identification using established evaluation performance measures. The benchmarking dataset of the contest was created with the help of 26 writers that were asked to copy eight pages that contain text in several languages (English, French, German and Greek). This paper describes the contest details including the evaluation measures used as well as the performance of the 8 submitted methods along with a short description of each method.

ICDAR2009 handwriting segmentation contest

ICDAR 2009 Handwriting Segmentation Contest was organized in the context of ICDAR2009 conference in order to record recent advances in off-line handwriting segmentation. The contest includes handwritten document images produced by many writers in several languages (English, French, German and Greek). These images are manually annotated in order to produce the ground truth which corresponds to the correct text line and word segmentation result. For the evaluation, a well-established approach is used based on counting the number of matches between the entities detected by the segmentation algorithm and the entities in the ground truth. This paper describes the contest details including the dataset, the ground truth and the evaluation criteria and presents the results of the 12 participating methods as well as of two state-of-the-art algorithms. A description of the winning algorithms is also given.

ICFHR 2012 Competition on Writer Identification Challenge 1: Latin/Greek Documents

Writer identification is important for forensic analysis, helping experts to deliberate on the authenticity of documents. The general objective of the ICFHR 2012 Writer Identification Contest is to record recent advances in the field of writer identification using established evaluation performance measures. Challenge 1 of the contest deals specifically with Latin scripts. The benchmarking dataset of challenge 1 of the contest was created with the help of 100 writers that were asked to copy four parts of text in two languages (English and Greek). This paper describes the contest details for this challenge including the evaluation measures used as well as the performance of the seven submitted methods along with a short description of each method.

ICDAR 2013 Competition on Writer Identification

Writer identification is important for forensic analysis, helping experts to deliberate on the authenticity of documents. The ICDAR2013 Competition on Writer Identification is part of a competition series (see also ICDAR2011 and ICFHR2012 Writer Identification Contests) which is dedicated to record recent advances in the field of writer identification for Latin scripts using established evaluation performance measures. The benchmarking dataset was created with the help of 250 writers that were asked to copy four parts of text in two Latin based languages (English and Greek). This paper describes the contest details including the evaluation measures used as well as the performance of the 12 submitted methods by 6 different groups along with a short description of each method.

Text Line Detection in Unconstrained Handwritten Documents Using a Block-Based Hough Transform Approach

In this paper we present a new text line detection method for unconstrained handwritten documents. The proposed technique is based on a strategy that consists of three distinct steps. The first step includes image binarization and enhancement, connected component extraction and average character height estimation. In the second step, a block-based Hough transform is used for the detection of potential text lines while a third step is used to correct possible splitting, to detect text lines that the previous step did not reveal and, finally, to separate vertically connected characters and assign them to text lines. The performance evaluation of the proposed approach is based on a consistent and concrete evaluation methodology.

ICFHR 2010 Handwriting Segmentation Contest

The general objective of the ICFHR 2010 Handwriting Segmentation Contest organized in the context of ICFHR 2010 conference was to use well established evaluation practices and procedures in order to record recent advances in off-line handwriting segmentation. Two new benchmarking datasets, one for text line and one for word segmentation, were created in order to test and compare recent algorithms for handwritten document segmentation in realistic circumstances. Handwritten document images were produced by many writers in several languages (English, French, German and Greek). The dataset of previously organized contest (ICDAR ICDAR 2009 Handwriting Segmentation Contest) was used as training dataset. This paper describes the contest details including the datasets, the ground truth, the evaluation criteria as well as the performance of the 7 submitted methods along with a short description of each method.

ICFHR 2014 Competition on Handwritten Keyword Spotting (H-KWS 2014)

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.