A Robust Zonal Fractal Dimension Method for the Recognition of Handwritten Telugu Digits

• Published in: International journal of image, graphics and signal processing graphics and signal processing, 2018-09, Vol.10 (9), p.42-55
• Main Authors: Subrahmanyam, MSLB, Kumar, V. Vijaya, Reddy, B. Eswara
• Format: Article
• Language: English
• Subjects: Character recognition; Classification; Digits; Feature extraction; Fractals; Handwriting; Handwriting recognition; Neural networks; Shape recognition; Statistical analysis; Statistical methods
• ISSN: 2074-9074; 2074-9082
• DOI: 10.5815/ijigsp.2018.09.06

Recognition of handwritten digits is most challenging sub task of character recognition due to various shapes, sizes, large variation in writing styles from person to person and also similarity in shapes of different digits. This paper presents a robust Telugu language handwritten digit recognition system. The Telugu language is most popular and one of classical languages of India. This language is spoken by more than 80 million people. The proposed method initially performs preprocessing on input digit pattern for removing noise, slat correction, size normalization and thinning. This paper divides the preprocessed Telugu handwritten digits into four differential zones of 2x2, 3x3, 4x4 and 6x6 pixels and extracts 65 features using Fractal dimension (FD) from each zone. The proposed zonal fractal dimension (ZFD) method uses, Feed forward backward propagation neural network (FFBPNN) for classifying the digits with learning rate of 0.01 and sigmoid function as an activation function on extracted 65 features. This paper evaluated the efficiency of the proposed method based on 5000 Telugu handwritten digit samples, each consists of ten digits from different groups of people and totally 50,000 samples. The performance of classification of the proposed method also evaluated using statistical parameters like recall, precision, F-measure and accuracy.