ICDAR2003 Page Segmentation Competition

There is a significant need to objectively evaluate layout analysis (page segmentation and region classification) methods. This paper describes the Page Segmentation Competition (modus operandi, dataset and evaluation criteria) held in the context of ICDAR2003 and presents the results of the evaluation of the candidate methods. The main objective of the competition was to evaluate such methods using scanned documents from commonly-occurring publications. The results indicate that although methods seem to be maturing, there is still a considerable need to develop robust methods that deal with everyday documents

ICFHR2016 Handwritten Keyword Spotting Competition (H-KWS 2016)

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] 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.This work was partially supported by the Spanish MEC under FPU grant FPU13/06281, by the Generalitat Valenciana under the Prometeo/2009/014 project grant ALMA-MATER, and through the EU projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, grant Ref. 674943).Pratikakis, I.; Zagoris, K.; Gatos, B.; Puigcerver, J.; Toselli, AH.; Vidal, E. (2016). ICFHR2016 Handwritten Keyword Spotting Competition (H-KWS 2016). IEEE. https://doi.org/10.1109/ICFHR.2016.0117

Automatic Document Image Binarization using Bayesian Optimization

Document image binarization is often a challenging task due to various forms of degradation. Although there exist several binarization techniques in literature, the binarized image is typically sensitive to control parameter settings of the employed technique. This paper presents an automatic document image binarization algorithm to segment the text from heavily degraded document images. The proposed technique uses a two band-pass filtering approach for background noise removal, and Bayesian optimization for automatic hyperparameter selection for optimal results. The effectiveness of the proposed binarization technique is empirically demonstrated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets

Digitisation Processing and Recognition of Old Greek Manuscipts (the D-SCRIBE Project)

After many years of scholar study, manuscript collections continue to be an important source of novel information for scholars, concerning both the history of earlier times as well as the development of cultural documentation over the centuries. D-SCRIBE project aims to support and facilitate current and future efforts in manuscript digitization and processing. It strives toward the creation of a comprehensive software product, which can assist the content holders in turning an archive of manuscripts into a digital collection using automated methods. In this paper, we focus on the problem of recognizing early Christian Greek manuscripts. We propose a novel digital image binarization scheme for low quality historical documents allowing further content exploitation in an efficient way. Based on the existence of closed cavity regions in the majority of characters and character ligatures in these scripts, we propose a novel, segmentation-free, fast and efficient technique that assists the recognition procedure by tracing and recognizing the most frequently appearing characters or character ligatures

tranScriptorium: a european project on handwritten text recognition

The tranScriptorium project aims to develop innovative, efficient and cost-effective solutions for annotating handwritten historical documents using modern, holistic Handwritten Text Recognition (HTR) technology. Three actions are planned in tranScriptorium: i) improve basic image preprocessing and holistic HTR techniques; ii) develop novel indexing and keyword searching approaches; and iii) capitalize on new, user-friendly interactive-predictive HTR approaches for computer-assisted operation.The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 600707 - tranScriptorium.Sánchez Peiró, JA.; Mühlberger, G.; Gatos, B.; Schofield, P.; Depuydt, K.; Davis, RM.; Vidal, E.... (2013). tranScriptorium: a european project on handwritten text recognition. ACM. https://doi.org/10.1145/2494266.2494294

Ground-Truth production in the tranScriptorium Project

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Tran Scriptorium is a 3-years project that aims to develop innovative, cost-effective solutions for the indexing, search and full transcription of historical handwritten document images, using Handwritten Text Recognition (HTR) technology. The production of ground-truth (GT) of a dataset of handwritten document images is among the first tasks. We address novel approaches for the faster production of this GT based on crowd-sourcing and on prior-knowledge methods. We also address here a novel low-cost semi-supervised procedure for obtaining pairs of correct line-level aligned detected/extracted text line images and text line transcripts, specially suitable for training models of the HTR technology employed in Tran Scriptorium.Work supported by the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement No.600707 - tranScriptorium.Gatos, B.; Louloudis, G.; Caser, T.; Grint, K.; Romero Gómez, V.; Sánchez Peiró, JA.; Toselli, AH.... (2014). Ground-Truth production in the tranScriptorium Project. En Document Analysis Systems (DAS), 2014 11th IAPR International Workshop on. IEEE Computer Society - Conference Publishing Services (CPS). 237-241. https://doi.org/10.1109/DAS.2014.23S23724

Transforming scholarship in the archives through handwritten text recognition:Transkribus as a case study

Purpose: An overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues. - Design/methodology/approach: This paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material. - Findings: Transkribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified. - Research limitations/implications: The paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc. - Practical implications: Only HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field. - Social implications: The increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals. - Originality/value: This is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector

A deep Convolutional Encoder-Decoder Network for Page Segmentation of Historical Handwritten Documents into Text Zones

Recent research activity for page segmentation and pixel-labeling problems focuses strongly on deep Neural Network architectures. In this paper, we present a Convolutional Encoder-Decoder based method for the segmentation of historical handwritten images into distinct text zones. This is achieved by labeling each pixel of the image to one of the predefined classes (main body, comments, decorations, periphery, background). Traditional methods make use of prior knowledge of documents and rely on data-oriented features and experimental rules. We propose a method using Convolutional Encoder-Decoder pairs and we show that deep architectures fit properly to our problem. Experiments on different public datasets demonstrate the effectiveness of the proposed method that outperforms previous techniques in many cases