What will you Learn in the Project?
- How to load IAM text and images data for model building
- Understanding the IAM data structure
- How to build deep learning models with CRNN architecture
- Understanding CTC for loss for text corrections
- Training and evaluating OCR model on the IAM dataset
Tools & Technologies Used
- Google Colab
- Pandas
- Numpy
- Matplotlib
- Tensorflow [Keras]
Tasks Performed
Task-1: Explore and Understand the IAM(words) dataset
Task-2: Explore and Understand the IAM(lines) dataset
Task-3: Explore and Understand the English-Hindi parallel corpus for translation
Task-4: Create a base offline OCR model using CRNN for IAM(words)
Task-5: Create a base offline OCR model using CRNN for IAM(lines)
Course Content
Tasks
Not Enrolled
Skills you will develop
Read and write data from text files and export it into a pandas data frame using Python
Load the dataset into memory using TensorFlow data API
Create distortion-free images with padding
Create a CTC loss class as a model layer
Create Edit Distance Callback class for calculating edit distance between predicted text and the actual label
Make predictions using a trained Deep Learning model and visualize those results and actual images
Video format is not supported, use Youtube video or MP4 format.
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