Data entry heavy processes
SUCCESS – 04
Data entry heavy processes
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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.
Tools Used
Machine Learning Technology
Platform Technology
RESULTS
Real-Time Analytics
0%
Reduction in
Processing Time
0%
Improvement
in Productivity
0%
TRANSFORMATION PROCESS
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.
- Feasibility Study
- Machine Learning Project Indication
- Technology Stack Selection
- Image & Text Recognition Model Architecture
- Model Creation
- Framework for Machine Learning Model
- Database Creation on MySQL
- User Testing and System Commissioning
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.