An investigation of the modified direction feature for cursive character recognition

Abstract

This paper describes and analyses the performance of a novel feature extraction technique for the recognition of segmented/cursive characters that may be used in the context of a segmentation-based handwritten word recognition system. The Modified Direction Feature (MDF) extraction technique builds upon the Direction Feature (DF) technique proposed previously that extracts direction information from the structure of character contours. This principal was extended so that the direction information is integrated with a technique for detecting transitions between background and foreground pixels in the character image. In order to improve on the DF extraction technique, a number of modifications were undertaken. With a view to describe the character contour more effectively, a redesign of the direction number determination technique was performed. Also, an additional global feature was introduced to improve the recognition accuracy for those characters that were most frequently confused with patterns of similar appearance. MDF was tested using a neural network-based classifier and compared to the DF and Transition Feature (TF) extraction techniques. MDF outperformed both DF and TF techniques using a benchmark dataset and compared favourably with the top results in the literature. A recognition accuracy of above 89% is reported on characters from the CEDAR dataset.

A Neural Approach to Cursive Handwritten Character Recognition Using Features Extracted from Binarization Technique

“…The purpose of feature extraction is to achieve most relevant and discriminative features to identify a symbol uniquely (Blumenstein et al 2007). Many feature extraction technique are proposed and investigated in the literature that may be used for numeral and character recognition.…”

“…Many feature extraction technique are proposed and investigated in the literature that may be used for numeral and character recognition. Consequently, recent techniques show very promising results for separated handwritten numerals recognition (Wang et al 2005), however the same accuracy has not been attained for cursive character classification (Blumenstein et al 2007). It is mainly due to ambiguity of the character without context of the entire word (Cavalin et al 2006).…”

Integrating RFID Technology with Intelligent Classifiers for Meaningful Prediction Knowledge

“…Two dominant training algorithms that have been proven to excel in network training are the Back-Propagation Algorithm [11,12] and the Evolutionary Neural Network [13,14]. Back-Propagation relies on the concept of training the network by propagating error back through the network via modifying the weights after the output has been calculated.…”

FLD Based Unconstrained Handwritten Kannada Character Recognition

“…Feature extraction method includes Template matching, Deformable templates, Unitary image transforms, Graph description, Projection histograms, Contour profiles, Zoning, Geometric moment invariants, Zernike moments, Spline curve approximation and Fourier descriptors. Different methods like neural network [7,8], Support vector machines [9], Fuzzy logic based [10] HCR are reported for the recognition of handwritten cursive words. Off-line Thai Handwriting recognition using Hidden Markov Model is also found in [11].…”