Reconstructing Japanese handwritten images using auto-encoder with residual block in parallel computing

Pratama, Mahardhika; Kareen, Pamela

doi:10.1109/iceltics.2017.8253262

Cited by 2 publications

(1 citation statement)

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“…The experimentations were carried out on MADBase Database for Arabic Handwritten digits images and it gave an average accuracy of 98.5%. Pratama et al [15] introduced recreating Japanese manually written images using auto encoder with residual block. The planned methodology expanded residual block between its input and output using parallel computing.…”

Section: Literature Reviewmentioning

confidence: 99%

Deformed character recognition using convolutional neural networks

Rani

Chandan²,

Jain³

et al. 2018

IJET

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Realization of high accuracies towards south Indian character recognition is one the truly interesting research challenge. In this paper, our investigation is focused on recognition of one of the most widely used south Indian script called Kannada. In particular, the proposed exper-iment is subject towards the recognition of degraded character images which are extracted from the ancient Kannada poetry documents and also on the handwritten character images that are collected from various unconstrained environments. The character images in the degraded documents are slightly blurry as a result of which character image is imposed by a kind of broken and messy appearances, this particular aspect leads to various conflicting behaviors of the recognition algorithm which in turn reduces the accuracy of recognition. The training of degraded patterns of character image samples are carried out by using one of the deep convolution neural networks known as Alex net.The performance evaluation of this experimentation is subject towards the handwritten datasets gathered synthetically from users of age groups between 18-21, 22-25 and 26-30 and also printed datasets which are extracted from ancient document images of Kannada poetry/literature. The datasets are comprised of around 497 classes. 428 classes include consonants, vowels, simple compound characters and complex com-pound characters. Each base character combined with consonant/vowel modifiers in handwritten text with overlapping/touching diacritics are assumed as a separate class in Kannada script for our experimentation. However, for those compound characters that are non-overlapping/touching are still considered as individual classes for which the semantic analysis is carried out during the post processing stage of OCR. It is observed that the performance of the Alex net in classification of printed character samples is reported as 91.3% and with reference to handwritten text, and accuracy of 92% is recorded.

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Section: Literature Reviewmentioning

confidence: 99%