Identify Handwriting Individually Using Feed Forward Neural Networks

Abstract

The paper justifies the necessity to use the hand writer identification using the feed forward neural networks. Identifying the authors of a handwritten sample using automatic image-based processing methods is an interesting pattern recognition problem with direct applicability in the legal and historic documents. Leading a worrisome life among the harder forms of biometrics, the identification of a writer on the basis of handwriting samples still remains a useful biometric modality, mainly due to its applicability in historical and the forensic field.

Handwritten objects recognition using Regularized Logistic Regression and feedforward Neural Networks

“…Now we will implement feed-fo the neural network [22]. We in return the neural network's implement the feed-forward com h (x (i) ) for every example i and predictions.…”

Ink width independent global features for writer verification

“…The size of the feature vector was 960 elements with accuracy of 96 % on CEDAR dataset. Anton C used multi-layer neural network for writer identification [6]. The letter 'h' is scanned at 600 dpi resolution and converted to binary image.…”