Welcome to a tutorial where we'll be discussing Convolutional Neural Networks (Convnets and CNNs), using one to classify dogs and cats with the dataset we built in the previous tutorial.

Text tutorials and sample code: https://pythonprogramming.net/....convolutional-neural

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Welcome to a tutorial where we'll be discussing how to load in our own outside datasets, which comes with all sorts of challenges!

First, we need a dataset. Let's grab the Dogs vs Cats dataset from Microsoft: https://www.microsoft.com/en-u....s/download/confirmat

Text tutorials and sample code: https://pythonprogramming.net/....loading-custom-data-

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Extracting texts of various sizes, shapes and orientations from images containing multiple objects is an important problem in many contexts, especially, in connection to e-commerce, augmented reality assistance system in a natural scene, content moderation in social media platform, etc. The text from the image can be a richer and more accurate source of data than human inputs which can be used in several applications like Attribute Extraction, Offensive Text Classification, Product Matching, Compliance use cases, etc. Extracting text is achieved in 2 stages. Text detection: The detector detects the character locations in an image and then combines all characters close to each other to form a word based on an affinity score which is also predicted by the network. Since the model is at a character level, it can detect in any orientation. Post this, the text is then sent through the Recognizer module. Text Recognition: Detected text regions are sent to the CRNN-CTC network to obtain the final text. CNN’s are incorporated to obtain image features that are then passed to the LSTM network as shown in the below figure. Connectionist Temporal Classification(CTC) decoder operation is then applied to the LSTM outputs for all the time steps to finally obtain the raw text from the image.

Key Takeaways:

1. Understanding the need for text extraction from Product Images.
2. Deep Learning Techniques for detecting highly oriented text.
3. End to End understanding of CRNN-CTC network for text recognition with TF 2.0
4. Need for CTC loss and theoretical understanding of the same.
5. Usage of Text Extraction in various fields/domains.

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This tutorial explains the few lines of code to visualize outputs of convolutional layers in any deep learning model.

Code generated in the video can be downloaded from here:
https://github.com/bnsreenu/py....thon_for_microscopis