A Combined System for Text Line Extraction and Handwriting Recognition in Historical Documents

Fischer, Andreas; Baechler, Micheal; Garz, Angelika; Liwicki, Marcus; Ingold, Rolf

doi:10.1109/das.2014.51

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…Pixel labeling assigns the same label to all the pixels of a text line [26,9,30]. Bounding polygon is used to enclose all the elements of a text line together with its neighbourhood background pixels [11,15]. Most of the extraction methods assume horizontally parallel text lines with constant heights, whereas some methods [5,2] are more generic.…”

Section: Related Workmentioning

confidence: 99%

Text Line Extraction Using Fully Convolutional Network and Energy Minimization

Barakat

Droby

Alaasam

et al. 2021

Lecture Notes in Computer Science

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Text lines are important parts of handwritten document images and easier to analyze by further applications. Despite recent progress in text line detection, text line extraction from a handwritten document remains an unsolved task. This paper proposes to use a fully convolutional network for text line detection and energy minimization for text line extraction. Detected text lines are represented by blob lines that strike through the text lines. These blob lines assist an energy function for text line extraction. The detection stage can locate arbitrarily oriented text lines.Furthermore, the extraction stage is capable of finding out the pixels of text lines with various heights and interline proximity independent of their orientations. Besides, it can finely split the touching and overlapping text lines without an orientation assumption. We evaluate the proposed method on VML-AHTE, VML-MOC, and Diva-HisDB datasets. The VML-AHTE dataset contains overlapping, touching and close text lines with rich diacritics. The VML-MOC dataset is very challenging by its multiply oriented and skewed text lines. The Diva-HisDB dataset exhibits distinct text line heights and touching text lines. The results demonstrate the effectiveness of the method despite various types of challenges, yet using the same parameters in all the experiments.

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Section: Related Workmentioning

confidence: 99%

Text Line Extraction Using Fully Convolutional Network and Energy Minimization

Barakat

Droby

Alaasam

et al. 2021

Lecture Notes in Computer Science

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“…In [32], the authors manage to detect initials, headings and text areas in historical documents. Other works focus on historical manuscripts to solve structure recognition [15,65] as well as text-line extraction [6,16,28].…”

Section: Bottom-up or Data-driven Strategiesmentioning

confidence: 99%

Combination of deep neural networks and logical rules for record segmentation in historical handwritten registers using few examples

Tarride

Lemaitre

Coüasnon

et al. 2021

IJDAR

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This work focuses on the layout analysis of historical handwritten registers, in which local religious ceremonies were recorded. The aim of this work is to delimit each record in these registers. To this end, two approaches are proposed. Firstly, object detection networks are explored, as three state-of-the-art architectures are compared. Further experiments are then conducted on Mask R-CNN, as it yields the best performance. Secondly, we introduce and investigate Deep Syntax, a hybrid system that takes advantages of recurrent patterns to delimit each record, by combining ushaped networks and logical rules. Finally, these two approaches are evaluated on 3708 French records (16-18th centuries), as well as on the Esposalles public database, containing 253 Spanish records (17th century). While both systems perform well on homogeneous documents, we observe a significant drop in performance with Mask R-CNN on heterogeneous documents, especially when trained on a non-representative subset. By contrast, Deep Syntax relies on steady patterns, and is therefore able to process a wider range of documents with less training data. Not only Deep Syntax produces 15% more match configurations and reduces the ZoneMap surface error metric by 30% when both systems are trained on 120 images, but it also outperforms Mask R-CNN when trained on a database three times smaller. As Deep Syntax generalizes better, we believe it can be used in the context of massive document processing, as collecting and annotating a sufficiently large and representative set of training data is not always achievable.

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“…The ancient document image analysis starts by segmenting a document in order to find and classify homogeneous regions or zones, such as graphic and textual regions [3]. Finding graphic regions can be used to segment and analyze the graphical part in historical heritage, such as the drop caps (i.e., the first letters at the beginning of a paragraph) [4] while determining text zones can be used as a pre-processing stage for character recognition [5], text line extraction [6] and handwriting recognition [7], etc. In this work, we are interested in ancient document image analysis.…”

Section: Introductionmentioning

confidence: 99%

Use of SLIC superpixels for ancient document image enhancement and segmentation

et al. 2015

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Designing reliable and fast segmentation algorithms of ancient documents has been a topic of major interest for many libraries and the prime issue of research in the document analysis community. Thus, we propose in this article a fast ancient document enhancement and segmentation algorithm based on using Simple Linear Iterative Clustering (SLIC) superpixels and Gabor descriptors in a multi-scale approach. Firstly, in order to obtain enhanced backgrounds of noisy ancient documents, a novel foreground/background segmentation algorithm based on SLIC superpixels, is introduced. Once, the SLIC technique is carried out, the background and foreground superpixels are classified. Then, an enhanced and non-noisy background is achieved after processing the background superpixels. Subsequently, Gabor descriptors are only extracted from the selected foreground superpixels of the enhanced gray-level ancient book document images by adopting a multi-scale approach. Finally, for ancient document image segmentation, a foreground superpixel clustering task is performed by partitioning Gabor-based feature sets into compact and well-separated clusters in the feature space. The proposed algorithm does not assume any a priori information regarding document image content and structure and provides interesting results on a large corpus of ancient documents. Qualitative and numerical experiments are given to demonstrate the enhancement and segmentation quality.

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A Combined System for Text Line Extraction and Handwriting Recognition in Historical Documents

Cited by 14 publications

References 22 publications

Text Line Extraction Using Fully Convolutional Network and Energy Minimization

Text Line Extraction Using Fully Convolutional Network and Energy Minimization

Combination of deep neural networks and logical rules for record segmentation in historical handwritten registers using few examples

Use of SLIC superpixels for ancient document image enhancement and segmentation

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