In this paper; we introduce a system of automatic recognition of Video Text Amazigh based on the Random Forest. After doing some pretreatments on the video and picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the convolutional neural network (CNN) and the Random Forest method. We carried out the experiments with a database containing 3300 samples collected from different writers. The experimental results show that our proposed OCR system is very efficient and provides good recognition accuracy rate of handwriting characters images acquired via Video camera phone.

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In this paper; we introduce a system of automatic recognition of Video Text Amazigh based on the Random Forest. After doing some pretreatments on the video and picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the convolutional neural network (CNN) and the Random Forest method. We carried out the experiments with a database containing 3300 samples collected from different writers. The experimental results show that our proposed OCR system is very efficient and provides good recognition accuracy rate of handwriting characters images acquired via Video camera phone.

In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels' densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.