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Data entry heavy processes – Nu-pie Management Consultancy Services

Image Processing and Handwritten Text Processing

Automating handwriting recognition systems can eliminate human error and the need for extensive manual data entry. These systems convert handwritten text into electronic format, making it easier to evaluate and process data electronically. By automating reporting requirements, businesses can reduce manual input, streamline the reporting process, and improve efficiency. Adopting automated solutions is essential to increase productivity while minimizing the risk of human error.

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Real-Time Analytics
Reduction in
Processing Time
0%
Improvement
in Productivity
0%
Feasibility Study

Based on the requirement of the task there was a need to first do a feasibility study.

Machine Learning Project Indication

It was a clear indicator that this was a machine learning project.

Technology Stack Selection

After a carful consideration we picked Python, TensorFlow and Deep learning models as the machine learning technology stack.

Image & Text Recognition Model Architecture

The next step was defining the architecture of the model that will support image processing and handwritten text recognition.

Model Creation

The model was created by:

  • Using existing libraries of similar images and handwritten text to start training the model.
  • b.The model was designed with the help of the deep learning models – Convolutional Neural Network (CNI) and Recurrent Neural Networks (RINI).
  • Evaluation metrics were defined by the process of annotation and labelling.
  • CoLab was used to train the model.
Framework for Machine Learning Model

The next step was to define the framework that will house the machine learning model. For this ReactJS and NodeJS was picked.

Database Creation on MySQL

The database for the program was created on MySQL.

User Testing and System Commissioning

The system was put together and commissioned only after an extensive user testing was done.

Based on the requirement of the task there was a need to first do a feasibility study.

It was a clear indicator that this was a machine learning project.

After a carful consideration we picked Python, TensorFlow and Deep learning models as the machine learning technology stack.

The next step was defining the architecture of the model that will support image processing and handwritten text recognition.

The model was created by:

a. Using existing libraries of similar images and handwritten text to start training the model. b.The model was designed with the help of the deep learning models – Convolutional Neural Network (CNI) and Recurrent Neural Networks (RINI).
 c. Evaluation metrics were defined by the process of annotation and labelling.
d. CoLab was used to train the model.

The next step was to define the framework that will house the machine learning model. For this ReactJS and NodeJS was picked.

The database for the program was created on MySQL.

The system was put together and commissioned only after an extensive user testing was done.