Characters recognition using keys points and convolutional neural network

Abstract

<p>In this paper, the convolutional neural network (CNN) is used in order to design an efficient optical character recognition (OCR) system for the Tifinagh characters. indeed, this approach has proved a greater efficiency by giving an accuracy of 99%, this approach based in keys points detection using Harris corner method, the detected points are automatically added to the original image to create a new database compared to the basic method that use directly the database after a preprocessing step consisting on normalization and thinning the characters. Using this method, we can benefit from the power of the convolutional neural network as classifier in image that has already the feature. The test was performed on the Moroccan Royal Institute of Amazigh Culture (IRCAM) database composed of 33000 characters of different size and style what present the difficulty, the keys points are the same in the printed and handwritten characters so this method can be apply in both type with some modifications.</p>

Handwritten Character Recognition in Assyrian Language Using Convolutional Neural Network

“…2022). Convolution is the first and most crucial stage in filtering a picture with a lower pixel filter to minimize its size while keeping pixel relationships (Boutounte & Ouadid, 2021). A 3x3 filter with a 1x1 stride (1-pixel shift at each step) convolutions the 5x5 picture to a 3x3 output (64% reduction in complexity) (Hossain & Ali, 2019).…”

“…Because pixels are only related to nearby pixels, convolution maintains the relationship between different parts of a picture(Lamsaf, Ait Kerroum, Boulaknadel, & Fakhri, 2022). Convolution is the first and most crucial stage in filtering a picture with a lower pixel filter to minimize its size while keeping pixel relationships(Boutounte & Ouadid, 2021). A 3x3 filter with a 1x1 stride (1-pixel shift at each step) convolutions the 5x5 picture to a 3x3 output (64% reduction in complexity)(Hossain & Ali, 2019).…”