Sign language is the major means of communication for the deaf community. It uses body language and gestures such as hand shapes, lib patterns, and facial expressions to convey a message. Sign language is geography-specific, as it differs from one country to another. Arabic Sign language is used in all Arab countries. The availability of a comprehensive benchmarking database for ArSL is one of the challenges of the automatic recognition of Arabic Sign language. This article introduces KArSL database for ArSL, consisting of 502 signs that cover 11 chapters of ArSL dictionary. Signs in KArSL database are performed by three professional signers, and each sign is repeated 50 times by each signer. The database is recorded using state-of-art multi-modal Microsoft Kinect V2. We also propose three approaches for sign language recognition using this database. The proposed systems are Hidden Markov Models, deep learning images’ classification model applied on an image composed of shots of the video of the sign, and attention-based deep learning captioning system. Recognition accuracies of these systems indicate their suitability for such a large number of Arabic signs. The techniques are also tested on a publicly available database. KArSL database will be made freely available for interested researchers.

The widely-used PDAs, touch screens, tablet-PCs are alternatives to keyboards with the advantages of being more friendly, easy, and natural. A framework for Arabic online character recognition is developed. The framework integrates the different phases of online Arabic text recognition. The used data poses several challenges such as delayed strokes handling, connectivity problems, variability, and style change of text. We process the delayed strokes at the different phases differently to improve the overall performance. This work includes feature extraction of many features, including several novel statistical features. Experimental results on challenging online Arabic characters show encouraging results.

Font Recognition (FR) is useful in improving optical text recognition accuracy and time. In addition, it can be used to restore the original document text fonts, styles and sizes. In this paper, we survey the literature of Arabic and Farsi FR research and used databases. The main phases of FR systems are surveyed (viz. preprocessing, classification techniques and used features). All published work of Arabic and Farsi FR, which the authors are aware of, are surveyed. To our knowledge, this is the first survey of Arabic/Farsi FR and used databases. In addition, the paper addresses the strengths and limitations of the presented techniques and specified areas of research that are not, so far, addressed in Arabic/Farsi FR as well as areas of possible improvement.

Sub-character HMM models for Arabic text recognition allow sharing of common patterns between different position-dependent shape forms of an Arabic character as well as between different characters. The number of HMMs gets reduced considerably while still capturing the variations in shape patterns. This results in a compact, efficient, and robust recognizer with reduced model set. In the current paper we are presenting our recent improvements in sub-character HMM modeling for Arabic text recognition where we use special 'connector' and 'space' models. Additionally we investigated contextual sub-characters HMMs for text recognition. We also present multi-stream contextual sub-character HMMs where the features calculated from a sliding window frame form one stream and its derivative features are part of the second stream. We report state-of-the-art results on the IFN/ENIT (benchmark) database of handwritten Arabic text and the recognition rate of 85.12% on sets outperforms previously published results.

This paper describes the first edition of the Arabic writer identification competition using AHTID/MW and KHATT databases held in the context of the 14th International Conference on Frontiers in Handwriting Recognition (ICFHR2014). This competition has used the new freely available Arabic Handwritten Text Images Database written by Multiple Writers (AHTID/MW) and the Arabic handwritten text database called KHATT presented in ICFHR2012. We propose three tasks in this Arabic writer identification competition: the first and second are based respectively on word and text line level using the AHTID/MW database and the third one is paragraph based using the KHATT database. We received one system for the second task, three systems for the third task and none for the first task. All systems are tested in a blind manner using a set of images kept internal. A short description of the participating groups, their systems, the experimental setup, and the observed results are presented.

Hidden Markov Model (HMM) is one of the most widely used classifier for text recognition. In this paper we are presenting novel sub-character HMM models for Arabic text recognition. Modeling at sub-character level allows sharing of common patterns between different contextual forms of Arabic characters as well as between different characters. The number of HMMs gets reduced considerably while still capturing the variations in shape patterns. This results in a compact and efficient recognizer with reduced model set and is expected to be more robust to the imbalance in data distribution. Experimental results using the sub-character model based recognition of handwritten Arabic text as well printed Arabic text are reported.

This paper presents a robust lexicon reduction technique using segment descriptors for Arabic handwritten text. The method segments an Arabic word into graphemes and adaptively generates a descriptor of the presence/absence of dots in those segments. The segmentation algorithm is based on the characteristic of Arabic script, which indicates predictable segmentations of Arabic characters. This in turn results in novel canonical segment descriptors for the lexicon entries. These descriptors are then used for lexicon reduction using a matching algorithm adapted for Arabic handwriting. Unlike other methods, features based on segment descriptors are computable for both word images and lexicon entries. Experimental results are reported on IfN/ENIT database which compare favorably with other approaches for lexicon reduction.

This article addresses writer identification of handwritten Arabic text. Several types of structural and statistical features were extracted from Arabic handwriting text. A novel approach was used to extract structural features that build on some of the main characteristics of the Arabic language. Connected component features for Arabic handwritten text as well as gradient distribution features, windowed gradient distribution features, contour chain code distribution features, and windowed contour chain code distribution features were extracted. A nearest neighbor (NN) classifier was used with the Euclidean distance measure. Data reduction algorithms (viz. principal component analysis [PCA], linear discriminant analysis [LDA], multiple discriminant analysis [MDA], multidimensional scaling [MDS], and forward/backward feature selection algorithm) were used. A database of 500 paragraphs handwritten in Arabic by 250 writers was used. The paragraphs used were randomly generated from a large corpus. NN provided the best accuracy in text-independent writer identification with top-1 result of 88.0%, top-5 result of 96.0%, and top-10 result of 98.5% for the first 100 writers. Extending the work to include all 250 writers and with the backward feature selection algorithm (using 54 out of 83 features), the system attained a top-1 result of 75.0%, top-5 result of 91.8%, and top-10 result of 95.4%.

In this paper, we report our comprehensive Arabic offline Handwritten Text database (KHATT) after completion of the collection of 1000 handwritten forms written by 1000 writers from different countries. It is composed of an image database containing images of the written text at 200, 300, and 600 dpi resolutions, a manually verified ground truth database that contains meta-data describing the written text at the page, paragraph, and line levels. A formal verification procedure is implemented to align the handwritten text with its ground truth at the form, paragraph and line levels. Tools to extract paragraphs from pages and segment paragraphs into lines are developed. Preliminary experiments on Arabic handwritten text recognition are conducted using sample data from the database and the results are reported. The database will be made freely available to researchers world-wide for research in various handwritten-related problems such as text recognition, writer identification and verification, etc.

Accurate automatic recognition of handwritten Arabic numerals has several important applications, e.g. in banking transactions, automation of postal services, and other data entry related applications. A number of modelling and machine learning techniques have been used for handwritten Arabic numerals recognition, including Neural Network, Support Vector Machine, and Hidden Markov Models. This paper proposes the use of abductive networks to the problem. We studied the performance of abductive network architecture on a dataset of 21120 samples of handwritten 0-9 digits produced by 44 writers. We developed a new feature set using histograms of contour points chain codes. Recognition rates as high as 99.03% were achieved, which surpass the performance reported in the literature for other recognition techniques on the same data set. Moreover, the technique achieves a significant reduction in the number of features required.

Fast Fourier transform (FFT) is used successfully in computing the Fourier descriptors which are used in object and character recognition. In this article, an Arabic character recognition algorithm using modified fourier spectrum (MFS) is presented. Ten descriptors are estimated from the Fourier spectrum of the character contour by subtracting the imaginary part from the real part (and not from the amplitude of the Fourier spectrum as is usually the case). Ten MFS descriptors are extracted and used for the recognition of Arabic characters. Experimental results using 10 MFS descriptors resulted in an average recognition rate of 95.9%. The analysis of the sparse matrix indicates that the major part of the errors is due to few similar characters. The new technique, based on MFS descriptors, was compared with the Fourier descriptors calculated from the amplitude of the FFT spectrum. Experimental results have shown that the MFS-based technique is faster to compute than the FFT-based technique. However, the Fourier descriptors, initially, have a better recognition rate than MFS descriptors (96.9% vs. 95.9%). Using the holes' and dots' features to resolve the problematic characters reduces the error rate of the MFS technique more than that of the Fourier descriptor technique. This article introduced MFS-based features that are faster to compute than Fourier descriptors and have fewer errors utilizing the dots and holes features of Arabic characters. Both techniques may be used in combination or in a multi-classifier system to enhance the Arabic recognition system rate.

Handwritten Arabic manuscripts have been highly underutilized owing to the fact that, to our knowledge, there is no system or interface in existence, utilizing which user can browse or search the desired information. Given vast content of these manuscripts, it is important to develop indexing systems that support content-based retrieval from historical manuscripts. In this paper, we propose a "Digital library framework for Arabic Manuscripts". A prototype system has been implemented which supports preprocessing of document images. To enhance their quality, feature extraction of the user identified words for similarity matching purposes is carried out. Utilizing relevance feedback from the user, the system indexes the manuscript pages for later efficient retrieval. The prototype system has been tested and encouraging results were achieved.

An automatic off-line character recognition system for handwritten cursive Arabic characters is presented. A robust noise-independent algorithm is developed that yields skeletons that reflect the structural relationships of the character components. The character skeleton is converted to a tree structure suitable for recognition. A set of fuzzy constrained character graph models (FCCGM's), which tolerate large variability in writing, is designed. These models are graphs, with fuzzily labeled arcs used as prototypes for the characters. A set of rules is applied in sequence to match a character tree to an FCCGM. Arabic handwritings of four writers were used in the learning and testing stages. The system proved to be powerful in tolerance to variable writing, speed, and recognition rate.>

A novel technique for motion estimation of moving objects is presented. The analysis of the Hartley spectrum indicates that the velocities of moving objects are related to the locations of the spectral peaks. The presented formulations and simulations demonstrate the applicability of this technique. This method is simple, fast, and computationally efficient.>