cBAD: ICDAR2017 Competition on Baseline Detection

Diem, Markus; Kleber, Florian; Fiel, Stefan; Grüning, Tobias; Gatos, Basilis

doi:10.1109/icdar.2017.222

Cited by 69 publications

(52 citation statements)

References 8 publications

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“…So, they proposed a new architecture where the layer convolution and max pooling is replaced by dilated convolutions. Their network has been trained on xheight labeling, reaching up to 75% of F-measure on the cBad dataset [12].…”

Section: Related Workmentioning

confidence: 99%

Arabic Handwritten Documents Segmentation into Text-Lines and Words using Deep Learning

Neche

Belaïd

Kacem-Echi

2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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One of the most important steps in a handwriting recognition system is text-line and word segmentation. But, this step is made difficult by the differences in handwriting styles, problems of skewness, overlapping and touching of text and the fluctuations of text-lines. It is even more difficult for ancient and calligraphic writings, as in Arabic manuscripts, due to the cursive connection in Arabic text, the erroneous position of diacritic marks, the presence of ascending and descending letters, etc. In this work, we propose an effective segmentation of Arabic handwritten text into text-lines and words, using deep learning. For text-line segmentation, we used an RU-net which allows a pixel-wise classification to separate text-lines pixels from the background ones. For word segmentation, we resorted to the text-line transcription, as we have not got a ground truth at word level. A BLSTM-CTC (Bidirectional Long Short Term Memory followed by a Connectionist Temporal Classification) is then used to perform the mapping between the transcription and text-line image, avoiding the need of the input segmentation. A CNN (Convolutional Neural Network) precedes the BLST-CTC to extract the features and to feed the BLSTM with the essential of the text-line image. Tested on the standard KHATT Arabic database, the experimental results confirm a segmentation success rate of no less than 96.7% for text-lines and 80.1% for words.

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Section: Related Workmentioning

confidence: 99%

Arabic Handwritten Documents Segmentation into Text-Lines and Words using Deep Learning

Neche

Belaïd

Kacem-Echi

2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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“…The metric used for the semantic segmentation is the F1 measure of pixels per class. Regarding the baseline evaluation, following the protocol of [15], we employed the evaluation toolkit as described in [17].…”

Section: Metricsmentioning

confidence: 99%

Semi-Synthetic Data Augmentation of Scanned Historical Documents

Karpinski

Belaïd

2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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This paper proposes a fully automatic new method for generating semi-synthetic images of historical documents to increase the number of training samples in small datasets. This method extracts and mixes background only images (BOI) with text only images (TOI) issued from two different sources to create semi-synthetic images. The TOIs are extracted with the help of a binary mask obtained by binarizing the image. The BOIs are reconstructed from the original image by replacing TOI pixels using an inpainting method. Finally, a TOI can be efficiently integrated in a BOI using the gradient domain, thus creating a new semi-synthetic image. The idea behind this technique is to automatically obtain documents close to real ones with different backgrounds to highlight the content. Experiments are conducted on the public HisDB dataset which contains few labeled images. We show that the proposed method improves the performance results of a semantic segmentation and baseline extraction task.

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“…Indeed, CNN-based methods obtained the most competitive results in the ICDAR2017 Competition on Baseline Detection in Archival Document (cBAD). The models were evaluated on several challenging datasets proposed by Diem et al [4] and composed of documents from 9 different archives.…”

Section: Related Workmentioning

confidence: 99%

Combination of Deep Learning and Syntactical Approaches for the Interpretation of Interactions Between Text-Lines and Tabular Structures in Handwritten Documents

Guerry

Coüasnon²,

Lemaitre

2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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In this article, we present our work on baseline detection in images of historical documents. This work focuses on handwritten documents containing tabular structures. One of the difficulties of this kind of documents is the strong interaction between text and tabular structures. This interaction leads to ambiguous cases for which recognition systems often over-or sub-segment baselines. The interest of our method is to combine contextual and structural knowledge in order to interpret properly this interaction. Our combination is able to merge heterogeneous information obtained with a deep-learning approach (for contextual elements) and a syntactical approach (for structural elements). Our grammatical description consists on a logical description of the intersections between text-lines and vertical rulings of detected tables. Intersections are described thanks to physical indicators extracted from images: vertical rulings, hypothetical text-lines, begin-and end-indicators of text-lines. We show on cBAD competition [4] (competition on baseline detection) that the combination of heterogeneous knowledge (structural and contextual information) improves baseline detection in handwritten documents. We obtain better scores than the best method published until now on this competition.

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cBAD: ICDAR2017 Competition on Baseline Detection

Cited by 69 publications

References 8 publications

Arabic Handwritten Documents Segmentation into Text-Lines and Words using Deep Learning

Arabic Handwritten Documents Segmentation into Text-Lines and Words using Deep Learning

Semi-Synthetic Data Augmentation of Scanned Historical Documents

Combination of Deep Learning and Syntactical Approaches for the Interpretation of Interactions Between Text-Lines and Tabular Structures in Handwritten Documents

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