Computers extract meaning from static handwritten text by processing an image, including separating characters from background noise. Processing text as it is being written often takes account of pen movement and uses special tablets.
The subsystems rely heavily on machine algorithms and deep-learning networks to generate hypotheses about how the ink strokes are segmented, the letters formed from the ink strokes, and the words formed from the letters. The interpretation subsystem identifies words and sentences based on the hypotheses about individual letters generated by the recognition system. The Recognition module passes what MyScript calls an ink object to the Interactive Ink Management module. In addition to handwritten text recognition, interactive ink recognizes handwritten mathematical notation, diagrams and geometric shapes, and musical notation.
On Thursday of this week, we will be looking at the parallel evolution of machine learning and handwriting recognition. Gary Baum, vice president of marketing for handwriting recognition software provider MyScript joins All Analytics Radio at 2 pm EDT. Maybe it's ironic that we're making progress in handwriting recognition at a time when school systems are considering eliminating cursive education. Register now for Thursday's streaming audio interview, How Machine Learning Takes Handwriting Recognition to New Levels, and join us Thursday, August 25, at 2:00 p.m. EDT.
So I set my goal on how to use a trained model using the easier TensorFlow MNIST tutorials on handwriting recognition. As expected, the model created form the second (expert) tutorial yielded better results in predicting the correct number form my handwriting. The difference between tutorial 1 and 2 is that the prediction in the model from the expert tutorial (model 2) uses the variable y_conv as the predicted label instead of y label in model 1 and that the prediction.eval The code snippet bellow shows the complete predictint() function to predict the correct integer and the main function to tie it all together (expert mode). The predictint() function takes the resulting pixel values from the imageprepare() function as input.
As an input method, handwriting recognition teaches machines to adapt to the user, adding in another layer to their evolving skill set. The team at MyScript Labs has a deep experience developing, customizing and adapting this technology to build out handwriting recognition systems for applications ranging from text and math, to shapes and music. Our knowledge base includes a variety of neural net architectures including perceptron, deep architecture, convolutional networks and recurrent networks among others. As an input method, handwriting recognition enables machines to adapt to the user's natural written input and adds another layer to their evolving skill set.
While handwriting recognition might be considered deep-learning 101, Japanese is a whole other ballgame. That's because the language includes symbolic characters such as kanji, which is composed of elements that can be read independently, making it difficult to know where one ends and another begins. There are also more than 2,000 common pictograms made up of dozens of strokes. The trick is to tackle one squiggle at a time.