Holistic cursive word recognition based on perceptual features

• Published in: Pattern recognition letters Vol. 28; no. 13; pp. 1600 - 1609
• Main Authors: Ruiz-Pinales, José, Jaime-Rivas, René, Castro-Bleda, María José
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2007; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Connectionism. Neural networks; Convolutional feature extraction; Exact sciences and technology; Holistic cursive script recognition; Information, signal and communications theory; Miscellaneous; Perceptual features; Signal processing; Speech processing; Telecommunications and information theory; Holistic cursive script recognition; Perceptual features; Convolutional feature extraction; Vocabulary; Grey level image; Line segment; Neural network; Baseline; Convolution; Speech recognition; Signal processing; Feature extraction; Feasibility; Speech processing
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2007.03.017

This work presents a holistic system for the off-line recognition of cursive words based on the extraction of perceptual features by means of convolution with templates of line segments. The method works directly on gray-level images to avoid losing information due to binarization. Features are represented spatially in order to preserve topological information which is well suited for recognition through convolutional neural networks. Moreover, our feature extraction method does not need to detect baselines to find ascenders and descenders. The system has been tested on small and medium vocabulary sizes demonstrating the feasibility of the proposed method.