Use Convolutional Recurrent Neural Network to recognize the Handwritten line text image without pre segmentation into words or characters. Use CTC loss Function to train.

Converts a text file to a handwritten pdf file.

Powerful handwritten text recognition. A simple-to-use, unofficial implementation of the paper "TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models".

Apply different text recognition services to images of handwritten documents.

End-to-end model training and deployment reference for handwritten Chinese text recognition, and can also be extended to other languages.

Computer characters to handwritten text converter.

Converts text into a PDF of handwritten notes

The repository provides access to the source code for Transcription Pearl, an Handwritten Text Recognition (HTR) tool, that uses AI to transcribe handwritten documents

A python OCR library to read and generation handwritten Cyrillic text

You can convert text/word word file to your handwriting. The results are indistinguishable from original handwritting.

Convert text file to handwritten pdf file

An E2E solution for Arabic Handwritten Text OCR, with an application to extract text, enhance camera-scanned documents, and grade handwritten exams based on a model answer.

Use Convolutional Recurrent Neural Network to recognize the Handwritten Word text image without pre segmentation into words or characters. Use CTC loss Function to train.

Handwritten Japanese OCR demo using touch panel to draw the input text using Intel OpenVINO toolkit

a quick and nice example app how to implement doodling and handwritten text recognition

Convert Arabic Handwritten Images to Text

The project of creating neural network possible to recognise Russian handwritten text

Handwritten text to synths!

NoteHub is an online note sharing platform where users can edit notes with a versatile rich-text editor in a real-time collaborative environment. NoteHub also provides notes sharing features between individuals or communities, and even more advanced features such as AI assistance, including content summarization, Q&A, voice to text transcription, and handwritten text recognition.

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An iOS app to turn typed text into images of handwritten text in your own handwriting style.

VNOnDB dataset extractor. This dataset can be use for build deep learning model to attack vietnamese handwritten text recognition problem.

A project which will convert plain text to handwritten like image

HandWritten is a streamlit application that converts a digital text document to a handwritten document

A LaTeX module for handwritten text - preserved from https://code.google.com/archive/p/handlatex/ and adapted to Python 3

Handwritten text recognition with sequence-to-sequence architecture

This example app shows how to recognize handwritten text using the Selvy Pen SDK for Text on Android.

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 Connectionist Temporal Classification (CTC) layer. As the input image is fed into the CNN layers, a non-linear ReLU function is applied to extract relevant features from the image. The ReLU function is preferred due to the lower likelihood of a vanishing gradient (which arises when network parameters and hyperparameters are not properly set) relative to a sigmoid function. In the case of the RNN layers, the Long Short-Term Memory (LSTM) implementation is used due to its ability to propagate information through long distances. The CTC is given the RNN output matrix and the ground truth text to compute the loss value and the mean of the loss values of the batch elements is used to train the OCR system. This means is fed into an RMSProp optimizer which is focused on minimizing the loss, and it does so in a very robust manner. For inference, the CTC layer decodes the RNN output matrix into the final text. The OCR system reports an accuracy rate of 95.7% for the IAM Test Dataset, but this accuracy falls to 89.4% for unseen handwritten doctors’ prescriptions.

A Simple Flask Application to convert Handwritten Kannada characters into printed text