debangan202ok / ocr_model Goto Github PK

1. TensorFlow
2. Python
3. Anaconda
4. OpenAi

Gather a large dataset of images with text. The images should be representative of the data your model will be working with in production.

Label the data with the correct text. This can be a time-consuming process, but it's crucial for supervised learning.

Choose a model architecture. Convolutional Neural Networks (CNNs) are commonly used for image-based tasks, and Long Short-Term Memory networks (LSTMs) are often used for sequence prediction like in OCR.

Train the model on your labeled data. This will likely involve splitting your data into a training set and a validation set, choosing an optimizer and loss function, and running the training process for a certain number of epochs.

Evaluate the model's performance on a test set of images that it hasn't seen during training.