Moftah Elzobi

Frame-Based facial expression recognition using geometrical features

To improve the human-computer interaction (HCI) to be as good as human-human interaction, building an efficient approach for human emotion recognition is required. These emotions could be fused from several modalities such as facial expression, hand gesture, acoustic data, and biophysiological data. In this paper, we address the frame-based perception of the universal human facial expressions (happiness, surprise, anger, disgust, fear, and sadness), with the help of several geometrical features. Unlike many other geometry-based approaches, the frame-based method does not rely on prior knowledge of a person-specific neutral expression; this knowledge is gained through human intervention and not available in real scenarios. Additionally, we provide a method to investigate the performance of the geometry-based approaches under various facial point localization errors. From an evaluation on two public benchmark datasets, we have found that using eight facial points, we can achieve the state-of-the-art recognition rate. However, this state-of-the-art geometry-based approach exploits features derived from 68 facial points and requires prior knowledge of the person-specific neutral expression. The expression recognition rate using geometrical features is adversely affected by the errors in the facial point localization, especially for the expressions with subtle facial deformations.

IESK-ArDB: a database for handwritten Arabic and an optimized topological segmentation approach

Even though a lot of researches have been conducted in order to solve the problem of unconstrained handwriting recognition, an effective solution is still a serious challenge. In this article, we address two Arabic handwriting recognition-related issues. Firstly, we present IESK-arDB, a new multi-propose off-line Arabic handwritten database. It is publicly available and contains more than 4,000 word images, each equipped with binary version, thinned version as well as a ground truth information stored in separate XML file. Additionally, it contains around 6,000 character images segmented from the database. A letter frequency analysis showed that the database exhibits letter frequencies similar to that of large corpora of digital text, which proof the database usefulness. Secondly, we proposed a multi-phase segmentation approach that starts by detecting and resolving sub-word overlaps, then hypothesizing a large number of segmentation points that are later reduced by a set of heuristic rules. The proposed approach has been successfully tested on IESK-arDB. The results were very promising, indicating the efficiency of the suggested approach.

Gabor Wavelet Recognition Approach for Off-Line Handwritten Arabic Using Explicit Segmentation

This article proposes an un-constrained recognition approach for the handwritten Arabic script. The approach starts by explicitly segment each word image into its constituent letters, then a filter-bank of Gabor wavelet transform is used to extract feature vectors corresponding to different scales and orientation in the segmented image. Classification is carried out by employing a support vectors machine algorithm, where IESK-arDB and IFN/ENIT databases are used for testing and evaluation of the proposed approach respectively. A Leave-one-out estimation strategy is followed to assess performance, where results confirmed the approach efficiency.

An Approach for Arabic Handwriting Synthesis Based on Active Shape Models

Comprehensive handwriting databases are crucial to train and test script recognition systems. However their generation is expensive in sense of manpower and time. As a result there is a lack of such databases which impedes research and development. This is especially true in case of holistic word recognition, since various samples must be available for each entry of the underlying vocabulary. To bypass this problem for Arabic, we present an efficient system that automatically generates images of synthetic handwritten words or text lines from unicode. A total of 28046 online samples of multiple writers are created to compute Active Shape Models (ASM) for over hundred letter classes. ASMs are used to generate unique letter representations for each synthesis. Subsequently these representations are modified by affine transformations, smoothed by B-Spline interpolation and composed to text. Finally the text is rendered and saved. In this way our system produces off-line pseudo handwritten samples with variations in shape and texture. We compare samples of the IFN/ENIT database with corresponding syntheses to show that these can be used to surrogate real samples.

Synthizing Handwritten Arabic Text Using Active Shape Models

This research paper proposes a template based approach for generating an unlimited number of synthesized handwritten Arabic characters. It starts by generating a polygonal representation for each training sample for every character class. Then for each class an Active Shape Model (ASM) is used to unify all polygonal samples in one compact representation. Accordingly, any desired number of synthesized characters, can be produced, as a result of simple linear combination between the Eigenvalues and Eigenvectors of the ASM. Ultimately, the contour of synthesized character is smoothed using piecewise cubic hermit interpolation. Moreover, by combining multiple synthesized characters, our system is capable of producing synthesized Arabic words. Even though experiments have shown that a perfect human- like handwriting is still far away. We think that our approach is a very promising and a step forward towards achieving this goal.

A Hidden Markov Model-Based Approach with an Adaptive Threshold Model for Off-Line Arabic Handwriting Recognition

In contrast to the mainstream HMM-based approaches dedicated for the recognition of offline handwritten Arabic, this paper proposes an HMM-based approach that built upon an explicit segmentation module. And shape representative based rather than sliding window based features, are extracted and used to build a reference as well as a confirmation model for each letter in each handwritten form. Additionally, we constructed an HMM-based threshold model by ergodically connecting all letter models, in order to detect false segmentation as well as nonletter segments. IESK-arDB and IFN/ENIT databases are used for testing and evaluation of the proposed approach respectively, and satisfactory results are achieved.

A structural features based segmentation for off-line handwritten Arabic text

Automatic Arabic handwritten text recognition is still an open research field, methods that describe satisfactory solution are still lacking. This can be attributed to cursive orthography and to the letter shape context sensitivity, which complex the problem of the character segmentation. This paper presents a heuristic rule based analytical segmentation approach for handwritten Arabic text, which preceded by a pre-process phase that handles binarization, short gaps closing, skew estimation, and critical features points calculation. Unlike other approaches a broader set of candidates for segmentation is generated and multi phase election process is performed to elect the best suitable candidates. Experiments are conducted on a database of 50 images of text sentences with an average of 4 words. Results were very satisfactory and outperform literature documented results.

Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research

Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers—that we proposed earlier—improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction.

CRFs and HCRFs Based Recognition for Off-Line Arabic Handwriting

This paper investigates the application of the probabilistic discriminative based Conditional Random Fields (CRFs) and its extension the hidden-states CRFs (HCRFs) to the problem of off-line Arabic handwriting recognition. A CRFs- and A HCRFs- based classifiers are built on top of an explicit word segmentation module using two different set of shape description features. A simple yet effective taxonomization technique is used to reduce the number of the class labels, and 3000 letter samples from IESK-arDB database are used for the training and 300 words are used for the evaluation. Experiments compare the performance of the CRFs to the HCRFs as well as to that of a generative based HMMs. Results indicate superiority of discriminative based approaches, where HCRFs achieved the best performance followed by CRFs.

ASM Based Synthesis of Handwritten Arabic Text Pages

Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs) based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available.