You will get Handwriting Recognition Machine Learning Application
You will get Handwriting Recognition Machine Learning Application
Handwriting Recognition Code
The client will get a Handwriting Recognition Machine Learning Code
- Delivery Time 1 day
- Number of Revisions 0
- Source Code
1 day
delivery —
Oct 16, 2024
Revisions may occur after this date.
Upwork Payment Protection
Fund the project upfront. SNEHA gets paid once you are satisfied with the work.
Project details
Pfactorial's Handwriting Recognition app is a great solution for digitizing your handwritten content. Thanks to its advanced machine learning algorithms, accuracy is guaranteed, no matter how complex your handwriting may be. From student notes to legal documents, research data, and more, this versatile app caters to various industries and purposes. Embrace a seamless and intuitive experience with its user-friendly interface, rendering the conversion process a breeze. This enhancement in work efficiency not only bolsters user productivity but also facilitates streamlined organisation and seamless sharing of ideas.
Machine Learning Tools
Azure Machine Learning, deeplearn.js, Deeplearning4j, Google AutoML, GPT-3, MLflow, NLTK, NumPy, OpenCV, pandas, PyMC, Python, Python Scikit-Learn, SciPy, Stanford CoreNLP, TensorFlow, Tesseract OCR, Vertex AI, XGBoostWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,000
|
Advanced
$2,000
|
|---|---|---|---|
| Delivery Time | 1 day | 1 day | 5 days |
Number of Revisions | 0 | 1 | 5 |
Model Validation/Testing | - | ||
Model Documentation | - | - | |
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
ADDITIONAL LAGUAGE
(+ 10 Days)
+$3,000
10 reviews
(10)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
LL
Lidija L.
Jul 25, 2023
Grammar & writing API familiarisation and assistance in migration
Not only did Sneha possess excellent technical skills, but she also exhibited a strong sense of professionalism and adaptability. She seamlessly integrated with our team and adapted to our project's requirements, making the entire process seamless and efficient. Sneha consistently went above and beyond, putting in extra effort to ensure the success of our project. Her dedication and commitment to delivering high-quality results were evident in every aspect of her work. Moreover, her communication skills were exceptional, making it easy to collaborate and discuss ideas effectively. Looking forward to collaborating on future projects.
TM
Tarun M.
Jun 25, 2023
Dynamics 365 F&O and PowerBi
RP
Raul P.
Mar 27, 2023
Natural Language Processing NLP Expert
I cannot recommend Sneha enough. As an nlp expert, she went above and beyond to deliver exceptional work. Her attention to detail made communication effortless and the project a pleasure to work on. Sneha consistently exceeded my expectations, delivering high-quality work that met all of my requirements.
PK
Philip K.
Apr 19, 2022
Social Media - Content Moderation Project
Sneha has been excellent at handling real-world data science/machine learning tasks. She is capable of handling complex research tasks independently and with minimal oversight. I highly recommend her. I plan to work with her again soon.
JS
Jaden S.
Apr 16, 2022
Write a script to batch process multiple files using an existing open source tool.
Sneha is a rising star! Her skillsets are mature and refined. She understands Python and NLP very well. I was impressed with the algorithms she developed. Her quality work is remarkable, she and went above and beyond my expectations. Sneha delivered professional results on time, and communicates well.
It was a pleasure working with Sneha. I will be hiring her again!
It was a pleasure working with Sneha. I will be hiring her again!
About SNEHA
Machine Learning/Deep Learning/NLP /DATA ANALYTICS CONSULTANT
100%
Job Success
Calicut, India - 12:26 pm local time
Summary of Skills:
Hands-on experience in ML models, DL models, NLP models, Computer Vision, Scraping , Algorithm development, Recommendation Engines, OpenAI, API building, Code optimisation, Language models
Programming skills :
Languages : Python , R, SQL
Database : MySql, Oracle, MongoDB, DynamoDB
Deep Learning: Hands on experience Deep learning architectures like RNN , LSTM , CNN, Transformers .Proficient in Tensorflow , Pytorch etc
NLP (Natural Language Processing):
Expert in Natural Language Processing with hands on experience on NLP libs in python like spacy, nltk , gnesim ,textblob, pytesseract , stanford core NLP ,fasttext, GLove models, GPT-2,BERT ,LDA topic modeling etc . Worked on Part-Of-Speech Tagging , Named Entity Detection, Sentiment analysis , Semantic Text Similarity, Text Summarisation , chunking , stemming , Lemmatization, Classification , Regression , Question and Answer models , Translational model, End to end, LAMA models, tf5, RoBERTa, Langchain, prompt engineering
Deep Learning models .
Computer Vision :
Expert in Deep learning architectures like , VGG ,Resnet, Darknet, Inseption ,Deep Learning techniques like Classification, Object detection , Segmentation. Familiar with libraries Tensorflow, Pytorch, H2O , Keras , fastai, fasttext ,OpenCV ,Pillow, sklearn, ChatGPT, GPT4, OpenAI, midjourney,
Machine Learning Engineer at Predmac Technologies: 2016 APR – 2021 JUL
Machine learning /AI developer at Pfactorial Technologies : 2021 JUL - Present
Project : NLP – Chapter recommendation engine for English learning platform
Role : Machine Learning Solution development.
Stack : Python , Django , MongoDB ,Spacy ,google cloud
Description : This project aimed at developing an English language learning platform for people with Chinese as first language. Lessons are given in form of Video content with subtitle. Different users have different learning curve. A personalised recommendation system was developed to suggest which chapter to take next. Model was build using Learners history, history of other users , Knowledge from English language etc
Projects :NLP- Legal contract OCR data tagging
Stack : Python , spacy ,deep learning models ,mysql db,amazon AWS .
Role : ML Engineer .
Description : Project aimed at organizations to manage agreement to maximizing opportunities, preserving options, and staying ahead of risks , leveraging the advances in Machine Learning and Natural Language Process. Developed many models using machine learning, and Natural Language Processing (NLP) to augment the human attorney review and tagging of key information’s in contract like type of contract ,important clauses ,dates , Party names ,monetary value etc.
Project : Supply chain Picker route optimisation using Deep learning
Role : Machine learning model development.
Stack : Python , Deep learning models ,on prem server .
Description : Develop an algorithm that finds an optimal path that minimizes the time required for a picker to retrieve required items in a warehouse. This requirement has a similar solution as Travelling salesman problem. A wrapper function over the tsp solver package solved for optimal path that abided to warehouse constraints. The number of possible case make finding optimal solution impossible with in the practical time limits. Deep learning was used here to reduce the time take for the algorithm. Resnet model was trained with one million pick list to predict delay .This is used to find optimal path, swapping between different picklists and computing the distance each time to find which list composition corresponds to minimum distance.
Project: Statistical Analysis in Health Care [ Identifying patient cohorts and outcomes ]
Role: Data Scientist
Stack : using R, python, cTAKES
Description: Project aimed at identifying the right data from the raw files obtained from the data bank of mainstream hospitals in US. From the identified data, framework was developed to identify PAH patients that resulted in a specific cohort and had outcomes in defined time ranges. Time to Event analysis (survfit), Kaplan Meyer curves, Sankey diagrams were used for analysis.
Project: Prediction model in Health Care [ BI-RADS and PH risk prediction]
Role: Machine learning/Computer Vision model development
Stack : using R, python
Description: Prediction of BI-RADS from images and processing of images for further analysis. Patient risk prediction for PH and subcategories like PAH.
Steps for completing your project
After purchasing the project, send requirements so SNEHA can start the project.
Delivery time starts when SNEHA receives requirements from you.
SNEHA works on your project following the steps below.
Revisions may occur after the delivery date.
Code transfer
We will share the code or push it to git