Lokesh Joshi (@lokeshjoshi053)
  • Stars
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  • Global Rank 1,611,161 (Top 56 %)
  • Followers 7
  • Registered over 4 years ago
  • Most used languages
    Rust
    25.0 %
    Dart
    25.0 %
    Solidity
    25.0 %
  • Location ๐Ÿ‡ฎ๐Ÿ‡ณ India
  • Country Total Rank 78,716
  • Country Ranking
    Solidity
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    Rust
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    Dart
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Top repositories

1

NFT-Smart-Contract

Smart Contract
Solidity
1
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2

Farming-Guru

A machine learning based website that recommends the best crop to grow, fertilizers to use, and the diseases caught by your crops.
Jupyter Notebook
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3

Optical-Character-Recognition-for-Hindi-Language-Android-Ios-App

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the documentยดs textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.
Dart
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4

Kron-Dex-Decentralized-Exchange

Kron Dex is a new project that runs on the Solana blockchain and is made using Rust programming language. It's a decentralized exchange where people can easily trade digital stuff like cryptocurrencies. It's super safe and fast because of Solana's technology and Rust's reliability. Kron Dex makes trading online assets easy and secure for everyone.
Rust
1
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