Developing an automatic signature verification system is challenging and demands a large number of training samples. This is why synthetic handwriting generation is an emerging topic in document image analysis. Some handwriting synthesizers use the motor equivalence model, the well-established hypothesis from neuroscience, which analyses how a human being accomplishes movement. Specifically, a motor equivalence model divides human actions into two steps: 1) the effector independent step at cognitive level and 2) the effector dependent step at motor level. In fact, recent work reports the successful application to Western scripts of a handwriting synthesizer, based on this theory. This paper aims to adapt this scheme for the generation of synthetic signatures in two Indic scripts, Bengali (Bangla), and Devanagari (Hindi). For this purpose, we use two different online and offline databases for both Bengali and Devanagari signatures. This paper reports an effective synthesizer for static and dynamic signatures written in Devanagari or Bengali scripts. We obtain promising results with artificially generated signatures in terms of appearance and performance when we compare the results with those for real signatures.
Abstract. This paper concerns automatic recognition of both printed and handwritten Bangla numerals. Such mixed numerals may appear in documents like application forms, postal mail, bank checks etc. Some pixel-based and shape-based features are chosen for the purpose of recognition. The pixel-based features are normalized pixel density over 4 X 4 blocks in which the numeral bounding-box is partitioned. The shape-based features are normalized position of holes, end-points, intersections and radius of curvature of strokes found in each block. A multi-layer neural network architecture was chosen as classifier of the mixed class of handwritten and printed numerals. For the mixture of twenty three different fonts of printed numerals of various sizes and 10,500 handwritten numerals, an overall recognition accuracy of 97.2% has been achieved.
Handwritten signature datasets are really necessary for the purpose of developing and training automatic signature verification systems. It is desired that all samples in a signature dataset should exhibit both inter-personal and intra-personal variability. A possibility to model this reality seems to be obtained through the synthesis of signatures. In this paper we propose a method based on motor equivalence model theory to generate static Bengali signatures. This theory divides the human action to write mainly into cognitive and motor levels. Due to difference between scripts, we have redesigned our previous synthesizer [1],[2], which generates static Western signatures. The experiments assess whether this method can approach the intra and interpersonal variability of the Bengali-100 Static Signature DB from a performance-based validation. The similarities reported in the experimental results proof the ability of the synthesizer to generate signature images in this script.
This paper describes the design of an ASIC chip for thinning of graylevel images. The chip implements a Min-Max skeletonization algorithm and is based on a pipeline architecture where each stage of the pipeline performs masking operations on the graylevel images. The chip operates in real time at a frequency of 8 MHz and utilizes about 321 mils × 410 mils of silicon area.
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