OCR, or Optical Character Recognition, is a fascinating technology that transforms the seemingly magical world of text in images into a realm of practical possibilities. Imagine being able to extract text from a photograph or a scanned document with just a few clicks. OCR systems are the sorcerers behind this incredible feat. Using complex algorithms and pattern recognition techniques, they analyze images or scanned documents, identify characters, and convert them into editable and searchable text. This wizardry opens up a multitude of applications, from digitizing printed materials to enhancing accessibility for visually impaired individuals. OCR systems have truly cast a spell on the way we interact with and manipulate text, making the impossible a reality with a wave of their virtual wands.
Explore our implementation goals, system design, performance analysis, and future work, as we aim to develop an accurate, efficient, and scalable OCR model.
Download the .h5 model from the repository and then test it using your own dataset.
- Download the given OCR_model.ipynb file.
- Follow the steps mentioned.
- All the datasets are uploaded in zip format in my Google Drive.
- Once the model is trained, test it on your dataset.
https://drive.google.com/drive/folders/1WskBeTKhqeXkSXeUx91TsdV6eGsJd4YK