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A TensorFlow implementation of https://github.com/bgshih/crnn
The Optical Character Recognition (OCR) system consists of a comprehensive neural network built using Python and TensorFlow that was trained on over 115,000 wordimages from the IAM On-Line Handwriting Database (IAM-OnDB). The neural network consists of 5 Convolutional Neural Network (CNN) layers, 2 Recurrent Neural Network (RNN) Layers, and a final
Handwritten Text Recognition (HTR) system implemented with TensorFlow (TF) and trained on the IAM off-line HTR dataset. This Neural Network (NN) model recognizes the text contained in the images of segmented words.
As part of the project we examine several approaches for recognizing text in images and predicting the whole digital text. All aproaches follow the method to break the image down into the smaller parts like lines, words or characters.
Handwritten text recognition system implemented with TensorFlow and trained on the the MNIST database of handwritten digits. This Neural Network model will recognise a single letter or word contained in the image. We will be using Python3, numpy, TensorFlow 2.3 and OpenCV.
Handwritten Text Recognition (HTR) system implemented with Tensor Flow (TF) and trained on the IAM off-line HTR data set. This Neural Network model recognises the text contained in the images of segmented words.
Through this project the user can upload an image of handwritten text and obtain the recognized text in txt format.
Starter repo for task 1 of the JPMC software engineering program
Starter repo for task 2 of JPMC's forage program
Tech stack : Android Studio in Java
An android application developed using android studio in java
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