Natural language morphology integration in off-line Arabic optical text recognition

Evolutionary Metric-Learning-Based Recognition Algorithm for Online Isolated Persian/Arabic Characters, Reconstructed Using Inertial Pen Signals

The development of sensors with the microelectromechanical systems technology expedites the emergence of new tools for human-computer interaction, such as inertial pens. These pens, which are used as writing tools, do not depend on a specific embedded hardware, and thus, they are inexpensive. Most of the available inertial pen character recognition approaches use the low-level features of inertial signals. This paper introduces a Persian/Arabic handwriting character recognition system for inertial-sensor-equipped pens. First, the motion trajectory of the inertial pen is reconstructed to estimate the position signals by using the theory of inertial navigation systems. The position signals are then used to extract high-level geometrical features. A new metric learning technique is then adopted to enhance the accuracy of character classification. To this end, a characteristic function is calculated for each character using a genetic programming algorithm. These functions form a metric kernel classifying all the characters. The experimental results show that the performance of the proposed method is superior to that of one of the state-of-the-art works in terms of recognizing Persian/Arabic handwriting characters.

COLLABORATIVE COMBINATION OF NEURON-LINGUISTIC CLASSIFIERS FOR LARGE ARABIC WORD VOCABULARY RECOGNITION

Most of the actual research in writing recognition focuses on specific applications where the vocabulary is relatively small. Many applications can be opened up when handling with large vocabulary. In this paper, we studied the classifier collaboration interest for the recognition of a large vocabulary of arabic words. The proposed approach is based on three classifiers, named Transparent Neuronal Networks (TNN), which exploit the morphological aspect of the Arabic word and collaborate for a better word recognition. We focused on decomposable words which are derived from healthy tri-consonantal roots and easy to proof the decomposition. To perform word recognition, the system extracts a set of global structural features. Then it learns and recognizes roots, schemes and conjugation elements that compose the word. To help the recognition, some local perceptual information is used in case of ambiguities. This interaction between global recognition and local checking makes easier the recognition of complex scripts as Arabic. Several experiments have been performed using a vocabulary of 5757 words, organized in a corpus of more than 17 200 samples. In order to validate our approach and to compare the proposed system with systems reported in ICDAR 2011 competition, extensive experiments were conducted using the Arabic Printed Text Image (APTI) database. The best recognition performances achieved by our system have shown very promising results.