Handwritten Recognition: A survey

• Published in: 2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS) pp. 199 - 205
• Main Authors: Al-Taee, May Mowaffaq, Neji, Sonia Ben Hassen, Frikha, Mondher
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09-12-2020
• Subjects: Biological neural networks; Character recognition; convolutional neural networks; Deep learning; Feature extraction; Handwriting recognition; Handwritten recognition; Image recognition; preprocessing; Training
• DOI: 10.1109/IPAS50080.2020.9334936

Handwritten recognition has received considerable attention in the domain of pattern recognition, image processing, over the last few decades. As a consequence of this research effort, several algorithms were developed using different techniques. Particularly, Deep Learning has shown a remarkable capability to handle handwritten recognition in very recent years. The well-known Deep learning techniques are the Convolutional Neuronal Networks (CNNs) and Recurrent Neuronal Networks (RNNs). This paper provides a survey of the most recent handwritten recognition systems. Thus, we present the most significant algorithms for handwritten character/word/text recognition by explaining the different approaches used in the recognition process and we compare them in terms of accuracy.