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.
Inking and navigating with a digital pen or stylus within Windows 10 will become easier within the Fall Creators Update, for those of you who use a tablet as, you know, a tablet. The improvements include two major elements: navigation, including using the pen or stylus to select and scroll text; and better interpretation of inked words as text, via a more accurate and responsive handwriting panel. Combined, it's a love letter of sorts to Surface and other tablet users who use the pen to input data. It's amazing how well Windows can interpret your chicken-scratch into text that can be edited in Word and elsewhere. General Windows 10 users won't be able to take advantage of the new features until the launch of the Fall Creators Update on Oct. 17.
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.
Driven by speech, handwriting and touch, our technologies are continually progressing towards intuitive communication between humans and machines, and we are continuing to march forward. Simple touch can include more complex input mechanisms using written input methods. Through handwriting recognition capabilities, neural network based input methods are now allowing consumers to interact with their devices simply by writing - including their wearables, smartphones and tablets. This enables users to write texts, emails or search for directions using handwriting recognition on screens that are too small to type on.
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.
Most sketch recognition systems are accurate in recognizing either text or shape (graphic) ink strokes, but not both. Distinguishing between shape and text strokes is, therefore, a critical task in recognizing hand drawn digital ink diagrams which commonly contain many text labels and annotations. We have found the ‘entropy rate’ to be an accurate criterion of classification. We found that the entropy rate is significantly higher for text strokes compared to shape strokes and can serve as a distinguishing factor between the two. Using entropy values, our system produced a correct classification rate of 92.06% on test data belonging to diagrammatic domain for which the threshold was trained on. It also performed favorably on data for which no training examples at all were supplied.