Handwritten Bangla Alphabet Recognition using an MLP Based Classifier

arXiv.org Artificial Intelligence

The work presented here involves the design of a Multi Layer Perceptron (MLP) based classifier for recognition of handwritten Bangla alphabet using a 76 element feature set Bangla is the second most popular script and language in the Indian subcontinent and the fifth most popular language in the world. The feature set developed for representing handwritten characters of Bangla alphabet includes 24 shadow features, 16 centroid features and 36 longest-run features. Recognition performances of the MLP designed to work with this feature set are experimentally observed as 86.46% and 75.05% on the samples of the training and the test sets respectively. The work has useful application in the development of a complete OCR system for handwritten Bangla text.


CEDAR Handwritten Address Interpretation

AITopics Original Links

HWAI Demonstration The Handwritten Address Interpretation System (HWAI) demo is designed to illustrate the steps involved in processing a handwritten address. The actual results are obtained without any human intervention.


Mathpix will solve handwritten math equations for you

#artificialintelligence

Today is the first time in a decade that I actually wished I was back in school. Mathpix is an iOS app that can recognize and answer handwritten math equations in seconds. Open the app, point the camera toward your math problem (you'll need legible handwriting, or it won't work properly), and it'll give you the correct answer along with step-by-step directions to reach the solution. Mathpix even works with more complex equations that require graphs or charts. The app works by sending the equation to a server, so you'll need a connection to use it.


RNN-based Online Handwritten Character Recognition Using Accelerometer and Gyroscope Data

arXiv.org Machine Learning

This abstract explores an RNN-based approach to online handwritten recognition problem. Our method uses data from an accelerometer and a gyroscope mounted on a handheld pen-like device to train and run a character pre-diction model. We have built a dataset of timestamped gyroscope and accelerometer data gathered during the manual process of handwriting Latin characters, labeled with the character being written; in total, the dataset con-sists of 1500 gyroscope and accelerometer data sequenc-es for 8 characters of the Latin alphabet from 6 different people, and 20 characters, each 1500 samples from Georgian alphabet from 5 different people. with each sequence containing the gyroscope and accelerometer data captured during the writing of a particular character sampled once every 10ms. We train an RNN-based neural network architecture on this dataset to predict the character being written. The model is optimized with categorical cross-entropy loss and RMSprop optimizer and achieves high accuracy on test data.