A Handwritten Equation Solver built using Convolutional Neural Network and Optical Character Recognition. CNN model is used for recognition of digits and symbols. OCR is used for processing the the image and segmentation.

In this Repository, there is a Detailed Explanation of Text Detection from Images. Text Detection using OCR(Optical Character Recognition). Its Done by pytesseract Library. Text Detection Includes Character Detection, Word Detection, Digit Detection.

Optical recognition of handwritten digits dataset -------------------------------------------------- **Data Set Characteristics:** :Number of Instances: 5620 :Number of Attributes: 64 :Attribute Information: 8x8 image of integer pixels in the range 0..16. :Missing Attribute Values: None :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr) :Date: July; 1998 This is a copy of the test set of the UCI ML hand-written digits datasets http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits The data set contains images of hand-written digits: 10 classes where each class refers to a digit.

Optical recognition of handwritten digits dataset wiht Naive bayes Algorithm

Optical Character Recognition of Handwritten Arabic Numerals/Digits using Artificial Nueral Network

Uses the K-Nearest Neighbor algorithm to classify handwritten digits.

System for visual digit recognition. Data come from US Postal Service (handwritten and scanned) and are digits from 0-9. Euclidean distance classifier is created in the principles of scikit-learn. Ensembling is also used, such us other known classifiers in scikit-learn.

This is Optical Character Recognition aka OCR. It recognizes the written, printed digits.

The classical problem in computer vision, image processing, and machine vision is that of determining whether or not the image data contains some specific object, feature, or activity. Different varieties of the recognition problem are described in the literature:[citation needed] Object recognition (also called object classification) – one or several pre-specified or learned objects or object classes can be recognized, usually together with their 2D positions in the image or 3D poses in the scene. Blippar, Google Goggles and LikeThat provide stand-alone programs that illustrate this functionality. Identification – an individual instance of an object is recognized. Examples include identification of a specific person's face or fingerprint, identification of handwritten digits, or identification of a specific vehicle. Detection – the image data are scanned for a specific condition. Examples include detection of possible abnormal cells or tissues in medical images or detection of a vehicle in an automatic road toll system. Detection based on relatively simple and fast computations is sometimes used for finding smaller regions of interesting image data which can be further analyzed by more computationally demanding techniques to produce a correct interpretation. Currently, the best algorithms for such tasks are based on convolutional neural networks. An illustration of their capabilities is given by the ImageNet Large Scale Visual Recognition Challenge; this is a benchmark in object classification and detection, with millions of images and 1000 object classes used in the competition.[29] Performance of convolutional neural networks on the ImageNet tests is now close to that of humans.[29] The best algorithms still struggle with objects that are small or thin, such as a small ant on a stem of a flower or a person holding a quill in their hand. They also have trouble with images that have been distorted with filters (an increasingly common phenomenon with modern digital cameras). By contrast, those kinds of images rarely trouble humans. Humans, however, tend to have trouble with other issues. For example, they are not good at classifying objects into fine-grained classes, such as the particular breed of dog or species of bird, whereas convolutional neural networks handle this with ease[citation needed]. Several specialized tasks based on recognition exist, such as: Content-based image retrieval – finding all images in a larger set of images which have a specific content. The content can be specified in different ways, for example in terms of similarity relative a target image (give me all images similar to image X), or in terms of high-level search criteria given as text input (give me all images which contain many houses, are taken during winter, and have no cars in them). Computer vision for people counter purposes in public places, malls, shopping centres Pose estimation – estimating the position or orientation of a specific object relative to the camera. An example application for this technique would be assisting a robot arm in retrieving objects from a conveyor belt in an assembly line situation or picking parts from a bin. Optical character recognition (OCR) – identifying characters in images of printed or handwritten text, usually with a view to encoding the text in a format more amenable to editing or indexing (e.g. ASCII). 2D code reading – reading of 2D codes such as data matrix and QR codes. Facial recognition Shape Recognition Technology (SRT) in people counter systems differentiating human beings (head and shoulder patterns) from objects

Recognising Handwritten digits with 98.89% accuracy, precision and f1-score

Optical character recognition which recognises handwritten digits using neural network. Algorithms applied are Stochastic gradient descent and Back propagation.

Optical digit recognition with BNN implemented in tm4c123gxl tiva C board, Needs a lot more optimization to improve the accuracy. (Needs more work )

Representing to you deep optical neural network designed for recognizing of handwritten digits. this project achieves accurate and efficient recognition of handwritten digits. Check out the codebase to delve into the realm of neural networks and witness the power of image classification in action

Handwritten Digit Prediction - Classification Analysis is to develop a highly accurate machine learning model that can classify handwritten digits (0-9) with precision and recall. The model should generalize well to new data, avoid overfitting, and be deployable in real-world applications, such as optical character recognition systems.

HW1, CS 445, Machine Learning, Winter 2012. A perceptron to classify the handwritten number data at http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits . Generalizable to data with features encoded as integers, with minor variations to code.

The 94-ASCII OCR Dataset is a comprehensive image dataset specifically designed for Optical Character Recognition (OCR) tasks, featuring 94 distinct ASCII characters. The dataset includes uppercase and lowercase English alphabets (A-Z, a-z), digits (0-9), and punctuation marks and special characters in many fonts.

In this porject, we have implemented eight different pattern recognition algorithms for classification of handwritten digits. Data sets are obtained from UCI machine learning repository, from which three different methods are introduced to exact the features. For each algorithm, we try it on all the three sets of features and setup several different configurations for the feasible features. At the end of the report, we compare the performance of the algorithms we used. Keywords: Optical Handwritten Digits Recognition, KNN, Gaussian, LBG-VQ, Kernel Perception, Neural Network, Nearest Group, LDA,SVM. You are encouraged to view a webpage version of this project at https://sites.google.com/site/eel6825group8project/home

Optical Recognition for Handwritten digits implemented in Java

Optical Character Recognition of Handwritten Digits

In this repository a handwritten Arabic digits OCR is implemented using TensorFlow Keras.

Optical Recognition of Handwritten Digits Data Set

Optical Recognition of Handwritten Digits using DNN

This is the final project for the course Probabilistic graphical models.

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Persian Digit Optical Character Recognition using Keras library

Use CNN and NN to train the Optical-recognition-of-handwritten-digits-dataset

Optical digit recognition using Tensorflow Lite Micro on the NM180100

A simple machine learning model I coded to predict hand-written digits from the MNIST data-set. I have also included Sci Kit Learn's implementation to allow for some comparison in accuracy scores.

Classifying Optical Recognition of Handwritten Digits dataset using various Machine Learning Algorithms