Käyttäjän chrizandr profiilikuva
Maan India lippu Sri City, Chittoor, India
Jäsen alkaen 25. toukokuuta 2017
0 Suositukset


Online-tilassa Offline-tilassa
Love coding, love reading, love learning. I am a professional when it comes to work. I complete most jobs on time and only bid on projects that I am sure I can do. I do not overcharge and my rates are reasonable. I am proficient in Python, Web Development(using Python), Machine Learning, Image Processing, Computer Vision. I am currently pursuing my Masters by Research in the field of Computer Vision. I am also open to hourly jobs, and long term projects, given a reasonable price.
$25 USD/hr
5 arvostelua
  • 100%Suoritetut työtehtävät
  • 96%Budjetin mukaisesti
  • 96%Ajallaan
  • 20%Uudelleenpalkkaamisaste


Viimeaikaiset arvostelut


Mentor for Google Summer of Code

Apr 2018

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Google Summer of Code Intern

Apr 2017 - Aug 2017 (4 months)

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Bachelor in Technology (Honours)

2014 - 2018 (4 years)

Higher Secondary Schooling

2012 - 2014 (2 years)


Consonant-Vowel Sequences as Subword Units for Code-Mixed Languages

We develop a state-of-art model for identifying sentiment in Hindi-English code-mixed language. We introduce new phonemic sub-word units for Hindi-English code-mixed text along with a hierarchical deep learning model which uses these sub-word units for predicting sentiment. The results indicate that the model yields a significant increase in accuracy as compared to other models.

Text Independent Writer Identification for Telugu Script using Directional Filter based Features

A new dataset of Telugu handwritten documents of 150 writers called the IIITS-THDB is created under this work for doing the experimental analysis and this dataset is made available publicly. Descriptive convolution based features are extracted from given handwritten text using directional filters. Feature reduction is performed to find the optimal linear subspace for the extracted features and identification is done using these reduced features.

Writer Identification in Indic Scripts: A Stroke Distribution Based Approach

A data driven approach for stroke alphabet creation is done as follows: strokes are extracted from the image, using a regression based method, extracted strokes are represented as fixed length vectors in a vector space, strokes are clustered in to stroke categories to create a stroke alphabet. An optimal number of clusters (categories) are automatically identified and a histogram is created that represents the writer's writing style.


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