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You're reading from  Modern Computer Vision with PyTorch

Product typeBook
Published inNov 2020
Reading LevelBeginner
PublisherPackt
ISBN-139781839213472
Edition1st Edition
Languages
Python
Tools
PyTorch
Concepts
Computer Vision
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Authors (2):
V Kishore Ayyadevara
V Kishore Ayyadevara
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V Kishore Ayyadevara

V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has 10 years' experience in data science, solving problems to improve customer experience in leading technology companies. In his current role, he is responsible for developing a variety of cutting edge analytical solutions that have an impact at scale while building strong technical teams. Prior to this, Kishore authored three books — Pro Machine Learning Algorithms, Hands-on Machine Learning with Google Cloud Platform, and SciPy Recipes. Kishore is an active learner with keen interest in identifying problems that can be solved using data, simplifying the complexity and in transferring techniques across domains to achieve quantifiable results.
Read more about V Kishore Ayyadevara

Yeshwanth Reddy
Yeshwanth Reddy
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Yeshwanth Reddy

Yeshwanth is a highly accomplished data scientist manager with 9+ years of experience in deep learning and document analysis. He has made significant contributions to the field, including building software for end-to-end document digitization, resulting in substantial cost savings. Yeshwanth's expertise extends to developing modules in OCR, word detection, and synthetic document generation. His groundbreaking work has been recognized through multiple patents. He also created a few Python libraries. With a passion for disrupting unsupervised and self-supervised learning, Yeshwanth is dedicated to reducing reliance on manual annotation and driving innovative solutions in the field of data science.
Read more about Yeshwanth Reddy

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Transcribing handwritten images

In the previous section, we learned about generating sequences of words from an input image. In this section, we will learn about generating sequences of characters with the image as input. Furthermore, we will learn about the CTC loss function, which helps in transcribing handwritten images.

Before we learn about the CTC loss function, let's understand the reason why the architecture that we saw in the image captioning section might not apply in handwritten transcription. Unlike in image captioning, where there is no straightforward correlation between the content in the image and the output words, in a handwritten image, there is a direct correlation between the sequence of characters present in the image and the sequence of output. Thus, we will follow a different architecture from what we designed in the previous section.

In addition, assume a scenario where an image is divided into 20 portions (assuming a scenario of a maximum of 20 characters...

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Modern Computer Vision with PyTorch
Published in: Nov 2020Publisher: PacktISBN-13: 9781839213472
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Authors (2)

author image
V Kishore Ayyadevara

V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has 10 years' experience in data science, solving problems to improve customer experience in leading technology companies. In his current role, he is responsible for developing a variety of cutting edge analytical solutions that have an impact at scale while building strong technical teams. Prior to this, Kishore authored three books — Pro Machine Learning Algorithms, Hands-on Machine Learning with Google Cloud Platform, and SciPy Recipes. Kishore is an active learner with keen interest in identifying problems that can be solved using data, simplifying the complexity and in transferring techniques across domains to achieve quantifiable results.
Read more about V Kishore Ayyadevara

author image
Yeshwanth Reddy

Yeshwanth is a highly accomplished data scientist manager with 9+ years of experience in deep learning and document analysis. He has made significant contributions to the field, including building software for end-to-end document digitization, resulting in substantial cost savings. Yeshwanth's expertise extends to developing modules in OCR, word detection, and synthetic document generation. His groundbreaking work has been recognized through multiple patents. He also created a few Python libraries. With a passion for disrupting unsupervised and self-supervised learning, Yeshwanth is dedicated to reducing reliance on manual annotation and driving innovative solutions in the field of data science.
Read more about Yeshwanth Reddy

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