Layout Analysis for Arabic Historical Document Images Using Machine Learning

Bukhari, Syed Saqib; Breuel, Thomas M.; Asi, Abedelkadir; El-Sana, Jihad

doi:10.1109/icfhr.2012.227

Cited by 54 publications

(35 citation statements)

References 15 publications

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“…Syed Saqib Bukhari et al 10 presented the application of a document management system to handle the problem of the automatic processing of documents available in film archives. They used machine learning algorithms for segmenting side notes from the main body text for complex Arabic documents.…”

Section: Requirements and Related Workmentioning

confidence: 99%

Software workflow for the automatic tagging of medieval manuscript images (SWATI)

et al. 2015

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Digital methods, tools and algorithms are gaining in importance for the analysis of digitized manuscript collections in the arts and humanities. One example is the BMBF-funded research project "eCodicology" which aims to design, evaluate and optimize algorithms for the automatic identification of macro-and micro-structural layout features of medieval manuscripts. The main goal of this research project is to provide better insights into high-dimensional datasets of medieval manuscripts for humanities scholars. The heterogeneous nature and size of the humanities data and the need to create a database of automatically extracted reproducible features for better statistical and visual analysis are the main challenges in designing a workflow for the arts and humanities.This paper presents a concept of a workflow for the automatic tagging of medieval manuscripts. As a starting point, the workflow uses medieval manuscripts digitized within the scope of the project "Virtual Scriptorium St. Matthias". Firstly, these digitized manuscripts are ingested into a data repository. Secondly, specific algorithms are adapted or designed for the identification of macro-and micro-structural layout elements like page size, writing space, number of lines etc. And lastly, a statistical analysis and scientific evaluation of the manuscripts groups are performed. The workflow is designed generically to process large amounts of data automatically with any desired algorithm for feature extraction. As a result, a database of objectified and reproducible features is created which helps to analyze and visualize hidden relationships of around 170,000 pages. The workflow shows the potential of automatic image analysis by enabling the processing of a single page in less than a minute. Furthermore, the accuracy tests of the workflow on a small set of manuscripts with respect to features like page size and text areas show that automatic and manual analysis are comparable. The usage of a computer cluster will allow the highly performant processing of large amounts of data. The software framework itself will be integrated as a service into the DARIAH infrastructure to make it adaptable for wider range of communities.

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Section: Requirements and Related Workmentioning

confidence: 99%

Software workflow for the automatic tagging of medieval manuscript images (SWATI)

et al. 2015

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“…Artificial Neural Networks were further tested on Arabic document layout analysis schemes. Bukhari et al [14] differentiated the central body and the side manuscript by applying the Multilayer Perceptron (MLP) classifier. A dataset is created which includes 38 historical document images and they achieved 95% classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers

Can

Kabadayı

2020

Applied Sciences

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Historical manuscripts and archival documentation are handwritten texts which are the backbone sources for historical inquiry. Recent developments in the digital humanities field and the need for extracting information from the historical documents have fastened the digitization processes. Cutting edge machine learning methods are applied to extract meaning from these documents. Page segmentation (layout analysis), keyword, number and symbol spotting, handwritten text recognition algorithms are tested on historical documents. For most of the languages, these techniques are widely studied and high performance techniques are developed. However, the properties of Arabic scripts (i.e., diacritics, varying script styles, diacritics, and ligatures) create additional problems for these algorithms and, therefore, the number of research is limited. In this research, we first automatically spotted the Arabic numerals from the very first series of population registers of the Ottoman Empire conducted in the mid-nineteenth century and recognized these numbers. They are important because they held information about the number of households, registered individuals and ages of individuals. We applied a red color filter to separate numerals from the document by taking advantage of the structure of the studied registers (numerals are written in red). We first used a CNN-based segmentation method for spotting these numerals. In the second part, we annotated a local Arabic handwritten digit dataset from the spotted numerals by selecting uni-digit ones and tested the Deep Transfer Learning method from large open Arabic handwritten digit datasets for digit recognition. We achieved promising results for recognizing digits in these historical documents.

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“…Page layout analysis methods can be categorized into three classes: granular-based, block-based and texture-based methods. Granular-based techniques [7]- [12] group basic layout entities of the page, e.g., pixels or connected components, to form larger homogeneous regions. Block-based approaches [13]- [15] segment the image into regions and subsequent splitting and merging steps are applied until yielding homogeneous regions.…”

Section: Introductionmentioning

confidence: 99%

Simplifying the reading of historical manuscripts

Asi

Cohen

Kedem

et al. 2015

2015 13th International Conference on Document Analysis and Recognition (ICDAR)

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Complex document layouts pose prominent challenges for document image understanding algorithms. These layouts impose irregularities on the location of text paragraphs which consequently induces difficulties in reading the text. In this paper we present a robust framework for analyzing historical manuscripts with complex layouts. This framework aims to provide a convenient reading experience for historians through topnotch algorithms for text localization, classification and dewarping. We segment text into spatially coherent regions and text-lines using texture-based filters and refine this segmentation by exploiting Markov Random Fields (MRFs). A principled technique is presented for dewarping curvy text regions using a non-linear geometric transformation. The framework has been validated using a subset of a publicly available dataset of historical documents and it provided promising results.

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Layout Analysis for Arabic Historical Document Images Using Machine Learning

Cited by 54 publications

References 15 publications

Software workflow for the automatic tagging of medieval manuscript images (SWATI)

Software workflow for the automatic tagging of medieval manuscript images (SWATI)

Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers

Simplifying the reading of historical manuscripts

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