Offline Handwritten Devanagari Character Identification

Industrial internet of things has grown quite popular in recent years and involves a large number of intelligent devices linked together to build a system that can investigate, communicate, gather and observe information. Due to this requirement, there is more demand for compression techniques which compresses data, leading to less usage of resources and low complexity. This is where Convolutional Neural Networks (CNN) play a large role in the field of computer vision, especially in places where high applications such as interpretation coupled with detection is required. Similarly, low-level applications such as image compression cannot be resolved using this methodology. In this paper, a compression technique for remote sensing images using CNN is proposed. This methodology incorporates CNN in a compact learning environment wherein the actual image that consists of structural data is coded using Lempel Ziv Markov chain algorithm. This process is followed by image reconstruction in order to obtain the actual image in high quality. Other methodologies such as optimized trunctiona, JPEG2000, JPEC and binary tree were compared using a large number of experiments in terms of space saving, reconstructed image quality and efficiency. The output obtained indicates that the proposed methodology shows effective improvement, attaining a 50 dB signal to noise ratio and space saving of 90%.

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Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization

Hamdan¹,

Sathesh²

2021

JIIP

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Due to the complex and irregular shapes of handwritten text, it is challenging to spot and recognize the handwritten words. In low-resource scripts, retrieval of words is a difficult and laborious task. The need for increasing the number of samples and introducing variations in the extended training datasets occur with the use of deep learning and neural network models. All possible variations and occurrences cannot be covered in an efficient manner with the use of the existing preprocessing strategies and theories. A scalable and elastic methodology for wrapping the extracted features is presented with the introduction of an adversarial feature deformation and regularization module in this paper. In the original deep learning framework, this module is introduced between the intermediate layers while training in an alternative manner. When compared to the conventional models, highly informative features are learnt in an efficient manner with the help of this setup. Extensive word datasets are used for testing the proposed model, which is built on popular frameworks available for word recognition and spotting, while enhancing them with the proposed module. While varying the training data size, the results are recorded and compared with the conventional models. Improvement in the mAP scores, word-error rate and low data regime is observed from the results of comparison.

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Offline Handwritten Devanagari Character Identification

Cited by 3 publications

References 11 publications

Introduction to data deduplication approaches

Introduction to data deduplication approaches

Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data

Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization

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