Nastaaligh handwritten word recognition using a continuous-density variable-duration HMM

Abstract EN

This paper introduces a complete system for recognition of Farsi Nastaaligh handwritten words using a continuous-density variable-duration hidden Markov model, CDVDHMM [1].

In preprocessing stage, after binarization, noise reduction, and connected component specification, new algorithms are applied to find and eliminate ascenders, descenders, dots, and other secondary strokes from the original image.

Then a new segmentation algorithm based on analyzing upper contour and two other processes is applied.

The main goal of this algorithm is to avoid the undersegmentation problem.

Considering variable duration states in the system allows covering the over-segmentation problem.

By finding the right-to-left order, the sequence of obtained sub-characters is modeled by the CDVDHMM.

Eight features, including three Fourier descriptors and five structural and discrete features, are applied to represent symbols in the feature space.

This feature vector is invariant to size and shift.

The states in the model are considered as pure characters (without secondary strokes) plus some compound forms of characters in Nastaaligh handwriting style.

Thus, training the model becomes simple and does not need any re-estimation method.

In the training stage, some parameters of the model are obtained from the training image set and the others from the dictionary.

At the last stage, a modified version of Viterbi algorithm is applied for recognition.

This algorithm provides more than one globally best path and considers different positions and forms of letters as sub-states and also supports variable duration states.

Experiments on handwritten samples and a 50-word dictionary show very good performance of the system.