Projects
Cheque Deposition System
Building Handwriting Recognition deep learning model and preprocess the image before recognize the cheque using opencv python for different aspect ratio and scaling the image. Train Optical Character Recognition from the cheque and insert the extracted text into databases. Deployment approach was batch processing method.
Breast Cancer Detection
Requirement gathering and data transfer from cloud to local file system. Preprocessing/augmentation of the image dataset using python open cv - lib. Extracted the image features using UNET architecture in Keras framework. Trained and evaluated the model with the ground truth dataset.
Voicebot Product
Requirement gathering, Micro-service architechture design using Flask, Environment configuration and Designing product workflow configuration in MongoDB. Audio preprocessing using sox and built a dyanimic work flow based on user configuration.
Automatic Number Plate Recognition System (ANPR)
Detect and extract the number from the license plate plus identify vehicle type and model for a parking slot. Data collected using web scrapping and labelled/annotated using labelme tool. Built and trained the CNN using the pre-trained YOLO and SSD architecture. Designed model pipelines and hosted that as a web service using flask.
ChatBot Product
Requirement gathering, applied design patter to build the business flow and incorporated the ORM techniques for data handing using MongoDB. Text preprocessing using SpaCy and BERT for word embedding. Introduced AI to the chatbot application by integrating that with RNN (LSTM) model to predict the next user query based on the chat flow. Hosted the webservice as AWS ec2 instance.
Speech to text (Deep speech) / VoiceBot
Created a recurrent neural network and extracts audio signal features as input. Augmentation of training data by adding real-world sound/noise to reduce the error rate of the model. Generate the language model with n-gram techniques to predict the text from the speech.
Enhanced Customer Profiling
Customer Segmentation was done on the sales data to cluster the customers based on RFM by using K-Means Algorithm. Using Sales data, Customer Lifetime Value Prediction was done. Churn Prediction was used to predict the Customers who will not purchase with a plan
Automate Ticket Classification
Extracting the feature from text data using NLP and text preprocessing using python NLTK library. Build a deep learning model using LSTM for the output of text data in vectorization format.
BELL.CA, Quarter Sales Plan
Transforming the data into various stages like data cleaning, normalization, so that they can be analyzed to extract insights and improve business processes using python. Identification and resolution of data level issues that are impacting operation performance. Visualizing the data in different business approaches using a BI tool.
BELL.CA, Quarter Sales Plan
Import data from My-SQL OLTP tables daily and load it in HIVE external tables using Sqoop. Develop Hive queries to replicate existing business logic and store processed data in HDFS.
Gender and Age Identification
Detect the person from the live feed. Identify the gender and age category using deep face recognition model using triplet loss function
Business card extraction
Detect the bossiness card from open area. Change to the straiten image like geometrical transformation. Detect the text localization in the card image. After the text identification using OCR approach for text recognition.