OCR model that handles complex tables, forms, handwriting with full layout.

OCR software for recognition of handwritten text

(Pattern Recognition) Pytorch implementation of “HTR-VT: Handwritten Text Recognition with Vision Transformer”

OCR using MXNet Gluon. The pipeline is composed of a CNN + biLSTM + CTC. The dataset is from: http://www.fki.inf.unibe.ch/databases/iam-handwriting-database. You need to register and get a username and password from their website.

A Windows math workspace for screenshot OCR, handwriting-to-LaTeX, editing, preview, and symbolic computation, powered by pix2text and MathLive.

Handwriting keyboard for Linux using local OCR engine.

ScriptReader allows you to perform Optical Character Recognition (OCR) on your handwritten notes!

Handwriting OCR for Vietnamese Address using state-of-the-art CRNN model implemented with Tensorflow. This was a challenge proposed by the Cinnamon AI Marathon.

Easter2.0: IMPROVING CONVOLUTIONAL MODELS FOR HANDWRITTEN TEXT RECOGNITION

Handwritten recognition with slice+cnn+lstm/multidimentional lstm/cnn+lstm

text-to-handwriting with OCR!

Let's explore how we can extract text from forms

Using Tensorflow to classify the NIST Dataset 19 (Handwriting)

This is a english handwriting recognition project

Using TensorFlow to create a ResNet model to train a deep learning model for images. Using OpenCV to do some image processing and show image with boundary box.

OCR/handwriting recognition libraries comparison

No description available

ISI's Optical Character Recognition (OCR) software for machine-print and handwriting data

Word Segmentation done for handwritten text recogntion

Repository for contributions for Data Generation for Post-OCR correction of Cyrillic handwriting paper

Tensorflow implementation of handwritten sequence of small letters recognition.

Work relating to the OCR wish-list item "figure out an algorithm that would separate images into sets with no handwriting, little handwriting (mostly text typed or printed), lots of handwriting"

Enhance Supernote .note files with Apple Vision Framework OCR - local, private, fast, and accurate handwriting recognition with word-level bounding boxes

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.

Model Context Protocol (MCP) Server for Handwriting OCR

For specific needs, accurate identification of handwritten regions is essential. We offer a model that outputs both OCR detection boxes and a handwriting probability map. The training scripts and dataset will be open-sourced.

Research Project: Optical Character Recognition using OpenCV and Deep Learning

CPSC 578 Semester Project

A python GUI digital whiteboard with handwriting recognition (OCR)

This project offers an end-to-end Arabic OCR solution for handwritten and printed text on template-based documents. It uses DBNet detection model trained on Arabic handwriting and a robust Recognition model. The application enables batch recognition process of documents with the same type for real-world OCR tasks.