Automatic handwriting recognition is an important component for many applications in various fields It is a challenging problem that has received a lot of attention in the past three decades Research has focused on the recognition of Latin languages’ handwriting Fewer studies have been done for the Arabic language In this paper, we present a new dataset of Arabic letters written exclusively by children aged 7–12 which we call Hijja Our dataset contains 47,434 characters written by 591 participants In addition, we propose an automatic handwriting recognition model based on convolutional neural networks (CNN) We train our model on Hijja, as well as the Arabic Handwritten Character Dataset (AHCD) dataset Results show that our model’s performance is promising, achieving accuracies of 97% and 88% on the AHCD dataset and the Hijja dataset, respectively, outperforming other models in the literature

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Arabic handwriting recognition system using convolutional neural network

A set of benchmarks for Handwritten Text Recognition on historical documents

Nowadays, deep learning methods are employed in a broad range of research fields. The analysis and recognition of historical documents, as we survey in this work, is not an exception. Our study analyzes the papers published in the last few years on this topic from different perspectives: we first provide a pragmatic definition of historical documents from the point of view of the research in the area, then we look at the various sub-tasks addressed in this research. Guided by these tasks, we go through the different input-output relations that are expected from the used deep learning approaches and therefore we accordingly describe the most used models. We also discuss research datasets published in the field and their applications. This analysis shows that the latest research is a leap forward since it is not the simple use of recently proposed algorithms to previous problems, but novel tasks and novel applications of state of the art methods are now considered. Rather than just providing a conclusive picture of the current research in the topic we lastly suggest some potential future trends that can represent a stimulus for innovative research directions.

Deep Learning for Historical Document Analysis and Recognition—A Survey

Historical palm-leaf manuscript and early paper documents from Indian subcontinent form an important part of the world's literary and cultural heritage. Despite their importance, large-scale annotated Indic manuscript image datasets do not exist. To address this deficiency, we introduce Indiscapes, the first ever dataset with multi-regional layout annotations for historical Indic manuscripts. To address the challenge of large diversity in scripts and presence of dense, irregular layout elements (e.g. text lines, pictures, multiple documents per image), we adapt a Fully Convolutional Deep Neural Network architecture for fully automatic, instance-level spatial layout parsing of manuscript images. We demonstrate the effectiveness of proposed architecture on images from the Indiscapes dataset. For annotation flexibility and keeping the non-technical nature of domain experts in mind, we also contribute a custom, web-based GUI annotation tool and a dashboard-style analytics portal. Overall, our contributions set the stage for enabling downstream applications such as OCR and word-spotting in historical Indic manuscripts at scale.

Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts

The application of handwritten text recognition to historical works is highly dependant on accurate text line retrieval. A number of systems utilizing a robust baseline detection paradigm have emerged recently but the advancement of layout analysis methods for challenging scripts is held back by the lack of well-established datasets including works in non-Latin scripts. We present a dataset of 400 annotated document images from different domains and time periods. A short elaboration on the particular challenges posed by handwriting in Arabic script for layout analysis and subsequent processing steps is given. Lastly, we propose a method based on a fully convolutional encoder-decoder network to extract arbitrarily shaped text line images from manuscripts.

https://hal.archives-ouvertes.fr/hal-02167164/document

BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscripts

In this paper we present a new database with handwritten Arabic script. It is based on five books written by different writers from the years 1088–1451. We took 680 pages from these five books, and fully annotated them on the sub-word level. For each page we manually applied bounding boxes on the different sub-words and annotated the sequence of characters. It consists of 121,636 sub-word appearances consisted of 244,553 characters out of a vocabulary of 1,731 forms of sub-words. The database is described in detail and is designed for training and testing recognition systems for handwritten Arabic sub-words. This database is available for the purpose of research, and we encourage researchers to develop and test new methods using our database.

VML-HD: The historical Arabic documents dataset for recognition systems

Deep learning is a form of hierarchical learning, it consists of multiple layers of representations that gradually transform data into high level concepts. Deep learning has been providing the state of the art results for various computer vision problems. However, a typical deep leaning algorithm needs a large amount of data to train a deep model and guarantee the models ability to generalize. It is not easy to generate large labeled datasets and it is one of the main barriers to apply deep learning for many problems. Data augmentation schemes were introduced to overcome this limitation, by extending small available labeled datasets. In this work we experiment with extending a small labeled dataset of Arabic continuous subwords by an orders of magnitude. The labeled dataset, which consist of handwritten Arabic subwords is used to synthesize a large collection of labeled dataset. The synthesized subwords are based on one or multiple writing styles from the original labeled dataset. We also experiment with generating various printed forms of subwords. We include only Naskh font, as most of the Arabic historical manuscripts were written in this type of font. We train several convolutional neural networks using handwritten, printed and synthesized datasets and obtain encouraging results.

Experiment study on utilizing convolutional neural networks to recognize historical Arabic handwritten text

This paper presents layout analysis for historical Arabic documents using siamese network. Given pages from different documents, we divide them into patches of similar sizes. We train a siamese network model that takes as an input a pair of patches and gives as an output a distance that corresponds to the similarity between the two patches. We used the trained model to calculate a distance matrix which in turn is used to cluster the patches of a page as either main text, side text or a background patch. We evaluate our method on challenging historical Arabic manuscripts dataset and report the F-measure. We show the effectiveness of our method by comparing with other works that use deep learning approaches, and show that we have state of art results.

Layout Analysis on Challenging Historical Arabic Manuscripts using Siamese Network

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.

Text Line Extraction Using Fully Convolutional Network and Energy Minimization

Segmenting handwritten document images into regions with homogeneous patterns is an important pre-processing step for many document images analysis tasks. Hand-labeling data to train a deep learning model for layout analysis requires significant human effort. In this paper, we present an unsupervised deep learning method for page segmentation, which revokes the need for annotated images. A siamese neural network is trained to differentiate between patches using their measurable properties such as number of foreground pixels, and average component height and width. The network is trained that spatially nearby patches are similar. The network's learned features are used for page segmentation, where patches are classified as main and side text based on the extracted features. We tested the method on a dataset of handwritten document images with quite complex layouts. Our experiments show that the proposed unsupervised method is as effective as typical supervised methods.

Reem Alaasam

Papers

VML-HD: The historical Arabic documents dataset for recognition systems

Experiment study on utilizing convolutional neural networks to recognize historical Arabic handwritten text

Layout Analysis on Challenging Historical Arabic Manuscripts using Siamese Network

Text Line Extraction Using Fully Convolutional Network and Energy Minimization

Unsupervised Deep Learning for Handwritten Page Segmentation