Online Arabic Handwriting Recognition Research Articles

Temporal residual network based multi-head attention model for Arabic handwriting recognition

In this study, we developed a new system for online Arabic handwriting recognition based on temporal residual networks with multi-head attention model. The main idea behind the application of attention mechanism was to focus on the most relevant parts of the data by a weighted combination of all input sequences. Moreover, we applied beta elliptic approach to represent both kinematic and geometric aspects of the handwriting motion. This approach consists of representing the neuromuscular impulses involving during the writing act. In the dynamic profile, the curvilinear velocity can be fitted by an algebraic sum of overlapped beta functions, while the original trajectory can be rebuilt by elliptic arcs delimited between successive extremum velocity instants. The experiments were conducted on LMCA database containing the trajectory coordinates of 23141 Arabic handwriting letters, and showed very promising results that achieved the recognition rate of 97,12%

Improving the DBLSTM for on-line Arabic handwriting recognition

Various applications involved in the computer recognition of pen-input handwritten words, such as the online form filling, text editing, note taking, and so on. Therefore, a great deal of research work tries to improve the recognition rate of those online words recognition systems resulting in several effective methods. Relevant results related to Latin and Chinese scripts have been achieved. However, the Arabic script has been neglected so far, which stimulated us to propose a new online Arabic handwriting recognition system based on DBLSTM that relies on three techniques that were applied in order to enhance its performance. First, the dropout was applied, in different positions, to prevent overfitting. Then, ReLU and Maxout units were added, in different ways, to overcome the vanishing gradient problem. These proposed systems were tested on a large database ADAB to prove its performance against difficult conditions such as the variety of writers, the large vocabulary and the diversity of style. According to the experimental results and compared to the baseline system, the best tested architecture gives a reduction of 10.99% in label error rate.

Normalisation of handwriting speed for online Arabic characters recognition

In this paper, we propose a new solution for the problem of the unevenly distributing points along the stroke curve, in online Arabic handwriting recognition, due to the variation in writing speed. An algorithm based on linear interpolation is generally used to solve this problem. The main weakness of this algorithm is the missing of some information related to point density distributions in different stroke parts. This limitation is due to the use of only one re-sampling distance. In our approach, we propose to segment the stroke trajectory into several parts according to their densities and to classify them into three classes: high, medium and low density. Three re-sampling distances, instead of one, are suggested, each of them is associated to one class of densities. This solution is evaluated using NOUN dataset and it gives an excellent improvement in the recognition rate, up to 4%, compared to linear interpolation method.

Normalisation of handwriting speed for online Arabic characters recognition

In this paper, we propose a new solution for the problem of the unevenly distributing points along the stroke curve, in online Arabic handwriting recognition, due to the variation in writing speed. An algorithm based on linear interpolation is generally used to solve this problem. The main weakness of this algorithm is the missing of some information related to point density distributions in different stroke parts. This limitation is due to the use of only one re-sampling distance. In our approach, we propose to segment the stroke trajectory into several parts according to their densities and to classify them into three classes: high, medium and low density. Three re-sampling distances, instead of one, are suggested, each of them is associated to one class of densities. This solution is evaluated using NOUN dataset and it gives an excellent improvement in the recognition rate, up to 4%, compared to linear interpolation method.

Online Arabic handwriting recognition: a survey

Researches on handwriting recognition have known a great attention since it has been considered as a technological revolution in man-machines interfaces especially that handwriting has continued to persist as the most used mean of communication and recording information in day-to-day life. The challenging nature of handwriting recognition and segmentation has attracted the attention of researchers from academic and industry circles. The huge part of these researches deals with Latin and Chinese. Interest in Arabic script comes years later, and so the state of the art is less advanced. This survey describes the nature of this Arabic handwritten language and the basic concepts behind the recognition process. An overview of the state of the art of online Arabic handwriting recognition is presented. It is based on an extensive review of the literature in order to describe background in the field, discussion of the methods, and future research directions. It is the first survey to focus on online Arabic handwriting recognition and provide recognition rates and descriptions of database used for the discussed approaches.

On-line Arabic handwriting recognition competition

This paper describes the on-line Arabic handwriting recognition competition held at tenth International Conference on Document Analysis and Recognition (ICDAR in Proceedings of the 10th internation...

On-line Arabic handwriting recognition with templates

After a long period of focus on western and East Asian scripts there is now a general trend in the on-line handwriting recognition community to explore recognition of other scripts such as Arabic and various Indic scripts. One difficulty with the Arabic script is the number and position of diacritic marks associated to Arabic characters. This paper explores the application of a template matching scheme to the recognition of Arabic script with a novel algorithm for dynamically treating the diacritical marks. Template based systems are robust to conditions with scarce training data and in experiments the proposed system outperformed a reference system based on the promising state-of-the-art network technique of BLSTM. Experiments have been conducted in an environment similar to that of many handheld devices with promising results both in terms of memory consumption and response time.

On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm

One of the most promising methods of interacting with small portable computing devices, such as personal digital assistants, is the use of handwriting. In order to make this communication method more natural, we propose to observe visually the writing process on an ordinary paper and to automatically recover the pen trajectory from numerical tablet sequences. On the basis of this work, we developed a handwriting recognition system based on visual coding and genetic algorithm “GA”. The system is applied on Arabic script. In this paper, we will present the different steps of the handwriting recognition system. We focus our contribution on the encoding system and the fitness function conception used as basic steps of the GA. A new approach based on visual indices similarity is developed to calculate the evaluation function. We optimize the times cooling of our system to give the final output (proposed words). Several experimentations are developed using an Arabic data set words extracted from “LMCA” database elaborated in our laboratory by 24 participants. The results obtained are very promising and prove that our new method based on hybridization between visual codes and GA is a powerful method.