12,188 research outputs found
Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers
The massive amounts of digitized historical documents acquired over the last
decades naturally lend themselves to automatic processing and exploration.
Research work seeking to automatically process facsimiles and extract
information thereby are multiplying with, as a first essential step, document
layout analysis. If the identification and categorization of segments of
interest in document images have seen significant progress over the last years
thanks to deep learning techniques, many challenges remain with, among others,
the use of finer-grained segmentation typologies and the consideration of
complex, heterogeneous documents such as historical newspapers. Besides, most
approaches consider visual features only, ignoring textual signal. In this
context, we introduce a multimodal approach for the semantic segmentation of
historical newspapers that combines visual and textual features. Based on a
series of experiments on diachronic Swiss and Luxembourgish newspapers, we
investigate, among others, the predictive power of visual and textual features
and their capacity to generalize across time and sources. Results show
consistent improvement of multimodal models in comparison to a strong visual
baseline, as well as better robustness to high material variance
Sparse Radial Sampling LBP for Writer Identification
In this paper we present the use of Sparse Radial Sampling Local Binary
Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture
classification. By adapting and extending the standard LBP operator to the
particularities of text we get a generic text-as-texture classification scheme
and apply it to writer identification. In experiments on CVL and ICDAR 2013
datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA)
performance. Among the SOA, the proposed method is the only one that is based
on dense extraction of a single local feature descriptor. This makes it fast
and applicable at the earliest stages in a DIA pipeline without the need for
segmentation, binarization, or extraction of multiple features.Comment: Submitted to the 13th International Conference on Document Analysis
and Recognition (ICDAR 2015
Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey
Optical character recognition (OCR) is a vital process that involves the
extraction of handwritten or printed text from scanned or printed images,
converting it into a format that can be understood and processed by machines.
This enables further data processing activities such as searching and editing.
The automatic extraction of text through OCR plays a crucial role in digitizing
documents, enhancing productivity, improving accessibility, and preserving
historical records. This paper seeks to offer an exhaustive review of
contemporary applications, methodologies, and challenges associated with Arabic
Optical Character Recognition (OCR). A thorough analysis is conducted on
prevailing techniques utilized throughout the OCR process, with a dedicated
effort to discern the most efficacious approaches that demonstrate enhanced
outcomes. To ensure a thorough evaluation, a meticulous keyword-search
methodology is adopted, encompassing a comprehensive analysis of articles
relevant to Arabic OCR, including both backward and forward citation reviews.
In addition to presenting cutting-edge techniques and methods, this paper
critically identifies research gaps within the realm of Arabic OCR. By
highlighting these gaps, we shed light on potential areas for future
exploration and development, thereby guiding researchers toward promising
avenues in the field of Arabic OCR. The outcomes of this study provide valuable
insights for researchers, practitioners, and stakeholders involved in Arabic
OCR, ultimately fostering advancements in the field and facilitating the
creation of more accurate and efficient OCR systems for the Arabic language
Effective Geometric Restoration of Distorted Historical Document for Large-Scale Digitization
Due to storage conditions and material’s non-planar shape, geometric distortion of the 2-D content is widely present in scanned document images. Effective geometric restoration of these distorted document images considerably increases character recognition rate in large-scale digitisation. For large-scale digitisation of historical books, geometric restoration solutions expect to be accurate, generic, robust, unsupervised and reversible. However, most methods in the literature concentrate on improving restoration accuracy for specific distortion effect, but not their applicability in large-scale digitisation. This paper proposes an effective mesh based geometric restoration system, (GRLSD), for large-scale distorted historical document digitisation. In this system, an automatic mesh generation based dewarping tool is proposed to geometrically model and correct arbitrary warping historical documents. An XML based mesh recorder is proposed to record the mesh of distortion information for reversible use. A graphic user interface toolkit is designed to visually display and manually manipulate the mesh for improving geometric restoration accuracy. Experimental results show that the proposed automatic dewarping approach efficiently corrects arbitrarily warped historical documents, with an improved performance over several state-of-the-art geometric restoration methods. By using XML mesh recorder and GUI toolkit, the GRLSD system greatly aids users to flexibly monitor and correct ambiguous points of mesh for the prevention of damaging historical document images without distortions in large-scale digitalisation
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