Aletheia - An Advanced Document Layout and Text Ground-Truthing System for Production Environments

Clausner, Christian; Pletschacher, Stefan; Antonacopoulos, Apostolos

doi:10.1109/icdar.2011.19

Cited by 101 publications

(54 citation statements)

References 7 publications

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“…On the other hand automatically obtained line regions [6] were manually revised with the Aletheia tool [7] (see examples 3 The dataset is free available at the TRANSCRIPTORIUM web page. 4 http://www.transcribe-bentham.da.ulcc.ac.uk/td/Transcribe Bentham of extracted lines in Fig.…”

Section: Data Descriptionmentioning

confidence: 99%

ICFHR2014 Competition on Handwritten Text Recognition on Transcriptorium Datasets (HTRtS)

Sánchez

Romero

Toselli

et al. 2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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IEEESánchez Peiró, JA.; Romero Gómez, V.; Toselli, AH.; Vidal Ruiz, E. (2014) Abstract-A contest on Handwritten Text Recognition organised in the context of the ICFHR 2014 conference is described. Two tracks with increased freedom on the use of training data were proposed and three research groups participated in these two tracks. The handwritten images for this contest were drawn from an English data set which is currently being considered in the tranScriptorium project. The the goal of this project is to develop innovative, efficient and cost-effective solutions for the transcription of historical handwritten document images, focusing on four languages: English, Spanish, German and Dutch. For the English language, the so-called "Bentham collection" is being considered in tranScriptorium. It encompasses a large set of manuscripts written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). A small subset of this collection has been chosen for the present HTR competition. The selected subset has been written by several hands (Bentham himself and his secretaries) and entails significant varibilities and difficulties regarding the quality of text images and writting styles. Training and test data were provided in the form of carefully segmented line images, along with the corresponding transcripts. The three participants achieved very good results, with transcription word error rates ranging from 15.0% down to 8.6%.

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Section: Data Descriptionmentioning

confidence: 99%

ICFHR2014 Competition on Handwritten Text Recognition on Transcriptorium Datasets (HTRtS)

Sánchez

Romero

Toselli

et al. 2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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“…For the Balinese character dataset, Balinese philologists manually annotated the segment of connected components that represented a correct character in Balinese script from the word-level binarized images that were manually annotated [11,17,20] using Aletheia (http://www.primaresearch.org/tools/Aletheia) [62,63] (Figure 14). The Sundanese character dataset was annotated manually [22] (Figure 15).…”

Section: Datasetsmentioning

confidence: 99%

Benchmarking of Document Image Analysis Tasks for Palm Leaf Manuscripts from Southeast Asia

et al. 2018

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This paper presents a comprehensive test of the principal tasks in document image analysis (DIA), starting with binarization, text line segmentation, and isolated character/glyph recognition, and continuing on to word recognition and transliteration for a new and challenging collection of palm leaf manuscripts from Southeast Asia. This research presents and is performed on a complete dataset collection of Southeast Asian palm leaf manuscripts. It contains three different scripts: Khmer script from Cambodia, and Balinese script and Sundanese script from Indonesia. The binarization task is evaluated on many methods up to the latest in some binarization competitions. The seam carving method is evaluated for the text line segmentation task, compared to a recently new text line segmentation method for palm leaf manuscripts. For the isolated character/glyph recognition task, the evaluation is reported from the handcrafted feature extraction method, the neural network with unsupervised learning feature, and the Convolutional Neural Network (CNN) based method. Finally, the Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) based method is used to analyze the word recognition and transliteration task for the palm leaf manuscripts. The results from all experiments provide the latest findings and a quantitative benchmark for palm leaf manuscripts analysis for researchers in the DIA community.

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“…A relatively new scalable and robust system is presented in "Aletheia" [20]. The main advantage of this system is that, it's completely flexible, as it supports bottom-up as well as top-down ground truth generation schemes.…”

Section: Ground Truth Generationmentioning

confidence: 99%

A Novel Hybrid Binarization Technique for Images of Historical Arabic Manuscripts

Hassanien¹,

Abdelfattah²,

Amin³

et al. 2015

SIC

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Abstract:In this paper, a novel binarization approach based on neutrosophic sets and sauvola's approach is presented. This approach is used for historical Arabic manuscript images which have problems with types of noise. The input RGB image is changed into the NS domain, which is shown using three subsets, namely, the percentage of indeterminacy in a subset, the percentage of falsity in a subset and the percentage of truth in a subset. The entropy in NS is used for evaluating the indeterminacy with the most important operation "λ mean" operation in order to minimize indeterminacy which can be used to reduce noise. Finally, the manuscript is binarized using an adaptive thresholding technique. The main advantage of the proposed approach is that it preserves weak connections and provides smooth and continuous strokes. The performance of the proposed approach is evaluated both objectively and subjectively against standard databases and manually collected data base. The proposed method gives high results compared with other famous binarization approaches.

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Aletheia - An Advanced Document Layout and Text Ground-Truthing System for Production Environments

Cited by 101 publications

References 7 publications

ICFHR2014 Competition on Handwritten Text Recognition on Transcriptorium Datasets (HTRtS)

ICFHR2014 Competition on Handwritten Text Recognition on Transcriptorium Datasets (HTRtS)

Benchmarking of Document Image Analysis Tasks for Palm Leaf Manuscripts from Southeast Asia

A Novel Hybrid Binarization Technique for Images of Historical Arabic Manuscripts

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