Youssef Rachidi. Int. Journal of Engineering Research and Application www.ijera.com ISSN : 2248-9622, Vol. 7, Issue 2, ( Part -4) February 2017, pp.37-43 www.ijera.com DOI: 10.9790/9622- 0702043743 37 | Page Improvement of the Recognition Rate by Random Forest Youssef Rachidi *, Zouhir Mahani ** * (Laboratoire Image et Reconnaissance de Formes – Systèmes Intelligents et Communicants (IRF – SIC), Université Ibn Zohr Agadir, Maroc ** (Laboratoire Des Sciences de l’Ingénieur et Management de l’Energie, Université Ibn Zohr Agadir, Maroc ABSTRACT 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. Keywords: Handwritten Character Recognition, Mobile phone, Random Forest, Zoning, Zernike Moments. I. INTRODUCTION The automatic recognition of handwritten or printed characters remains a subject of research and experimentation. The problem is not yet solved despite the fact that results have reached fairly high rates in some applications [1]. Some attempts have been done to improve the current situation [1]. In this context, we have employed a recognition system of handwritten characters extracted from a picture taken by camera phone [2]. Indeed, in the primitives’ extraction stage, our approach is based on primitives of the Zoning types [3], of Diagonal [4], Horizontal and of the Zernike’s moment [5] [6] [7] [8]. These primitives will supply a Random Forest in the learning and recognizing phases. On a database of handwritten, segmented and isolated characters acquired by camera phone, obtained an encouraging results on the majority of this characters. The limit of this adopted approach is that it is not operational on some extracted characteristics of Zernike’s moments [9]. To rememdy these limits, we suggest a new method based on the Random Forest which should render the increase of the rate of recognition possible. Habitually, the phases form the structures of handwriting recognition system are: Pre-processing, Segmentation, Feature extraction, Classification and Post-processing [2]. In this paper, our objective is mainly interested in the development of handwriting character recognition system and Improvement of the Recognition Rate by Random Forest, in which the images are obtained by camera phone. The paper is organized as follows. In section II, the proposed the pre-processing and gives descriptions of the methods that we used throughout the OCR process, which includes the following stages: Binarization, Noise removing, skew detection and correction and Segmentation. The feature extraction procedure adopted in the system is detailed in the section III. Section IV describes the classification and recognition using propagation neural network and Random Forest. Section V presents the experimental results and comparative analysis. Finally, the paper is concluded in section VI. II. PRE-PROCESSING The procedure of preprocessing which refines the scanned input image includes several steps: Binarization, for transforming gray-scale images in to black and white images, noises removal, and skew correction performed to align the input paper document with the coordinate system of the scanner and segmentation into isolated characters [1]. 2.1 Binarization and Noise Remo- oval We used the Sauvola method for binarization [10] this method of thresholding is performed as a preprocessing step to remove the background noise from the picture prior to extraction of characters and recognition of text. Fig.3 (a) shows a sample input handwritten character image and Fig.3(b) shows the binarized image after the thresholding step using Sauvola method. RESEARCH ARTICLE OPEN ACCESS