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.

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

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

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.

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.

Google Cloud-based Handwriting Recognition app made in Python, React.JS, and leverages OCR functionality using Google Cloud Vision API.

AI-powered handwriting recognition app that converts handwritten notes into digital text. Built with Flask, OpenCV, Pytesseract, and React.js. Upload an image of handwritten text, and AI extracts the content for easy editing and sharing. 🚀 #OCR #AI #HandwritingRecognition

Python Computer Vision: OCR (Optical Character Recognition) for handwriting analysis. (kNN + OpenCV)

Intelligent Handwriting Character Recognition is one of the field left very less touched and worked upon. Various OCR engines work for English language but that too not handwritten data. The Aim to work on this project is, the processing of Handwritten images and extract something menaingful out of it. The stepwise approch used is quiet clear from the number system used for various directories.

NodeJs and Python Code Sample to Perform OCR and Handwriting Recognition

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OCR (optical character recognition) is the use of technology to distinguish printed or handwritten text characters inside digital images of physical documents, such as a scanned paper document. The basic process of OCR involves examining the text of a document and translating the characters into code that can be used for data processing. OCR is sometimes also referred to as text recognition. OCR systems are made up of a combination of hardware and software that is used to convert physical documents into machine-readable text. Hardware, such as an optical scanner or specialized circuit board is used to copy or read text while software typically handles the advanced processing. Software can also take advantage of artificial intelligence (AI) to implement more advanced methods of intelligent character recognition (ICR), like identifying languages or styles of handwriting.

Handwriting Recognition (OCR) using Deep Learning (Tensorflow)

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Chinese dictionary with HSK vocabulary, flashcards, OCR & handwriting recognition. Don't just search words; remember them!

The Imgscope-OCR-2B-0527 model is a fine-tuned version of Qwen2-VL-2B-Instruct, specifically optimized for messy handwriting recognition, document OCR, realistic handwritten OCR, and math problem solving with LaTeX formatting. This model is trained on custom datasets for document and handwriting OCR tasks and textual understanding

OCR - Optical Character Recognition. The issue of manual data entry in the majority of NGO’s is burdensome, inefficient and unmanageable. Therefore, we intend to build an OCR software for handwriting recognition. OCR is Optical Character Recognition and is the electronic translation of handwritten text into machine-editable text. We are inclined to develop a software which will allow to set up different types of forms in the system. The OCR software should be competent enough to recognise the handwritten-text.

Project has been developed using Deep Learning algorithms (CRNN+LSTM+CTC), OCR (Optical Character Recognition) and handwriting recognition, it can digitize and store prescriptions thus liberating the user from their hardship by introducing a medical prescription manager that stores all the prescriptions

TextWeave is a web-based OCR tool and API that converts handwritten or printed notes from image files into text. It uses EasyOCR powered by PyTorch for accurate handwriting recognition. This project supports single-image upload, non-editable OCR results, and text download functionality.

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Optical Character Recognition (OCR) is the process that converts an image of text into a machine-readable text format. For example, if you scan a form or a receipt, your computer saves the scan as an image file. You cannot use a text editor to edit, search, or count the words in the image file.

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Deep Learning OCR for Handwriting Recognition

OCR Model to detect dyslexic handwriting developed using handwritten datasets and applying CNN algorithm

Cloud-Based Handwriting OCR System A scalable solution using Tesseract and deep learning for accurate text extraction from handwritten documents. Features multi-language support, image preprocessing, and real-time processing via cloud APIs.