To wrap up today’s OCR tutorial, we’ll discuss our handwriting recognition results, including what worked and what didn’t. We’ll review our project structure and then implement a Python script to perform handwriting recognition with OpenCV, Keras, and TensorFlow. You should have a firm understanding of the concepts and scripts from last week as a prerequisite for this tutorial. Note: If you haven’t read last week’s post, I strongly suggest you do so now before continuing, as this post outlines the model that we trained to OCR alphanumeric samples. I’ll then provide a brief review of the process for training our recognition model using Keras and TensorFlow - we’ll be using this trained model to OCR handwriting in this tutorial.
In the first part of this tutorial, we’ll discuss handwriting recognition and how it’s different from “traditional” OCR.
#OCR FONT OCR CODE#
Looking for the source code to this post? Jump Right To The Downloads Section OCR: Handwriting recognition with OpenCV, Keras, and TensorFlow
#OCR FONT OCR HOW TO#
To learn how to perform handwriting recognition with OpenCV, Keras, and TensorFlow, just keep reading. I truly think you’ll find value in reading the rest of this handwriting recognition guide. You’ll see examples of where handwriting recognition has performed well and other examples where it has failed to correctly OCR a handwritten character. Today’s tutorial will serve as an introduction to handwriting recognition. We’re not there yet, but with the help of deep learning, we’re making tremendous strides. Handwriting recognition is arguably the “holy grail” of OCR. These variations in handwriting styles pose quite a problem for Optical Character Recognition engines, which are typically trained on computer fonts, not handwriting fonts.Īnd worse, handwriting recognition is further complicated by the fact that letters can “connect” and “touch” each other, making it incredibly challenging for OCR algorithms to separate them, ultimately leading to incorrect OCR results. Talk about embarrassing! Truly, it’s a wonder they ever let me out of grade school. And on more than one occasion, I’ve had to admit that I couldn’t read them either. I’m often asked by those who read my handwriting at least 2-3 clarifying questions as to what a specific word or phrase is.
#OCR FONT OCR WINDOWS#
Tahoma is present on any Windows system and the main reason that this font works so well is that there is enough difference between look-a-like characters which make the OCR engine interpret each character correctly even without any context.Figure 2: As you can see, my handwriting leaves a little bit to be desired. Proportionally spaced means that for example the letter i takes less space than the letter w which makes text more readable and also more compact. Tahoma is a modern looking sans serif (no curls) proportionally spaced font. We tried a range of standard Windows fonts and we found out that the standard Tahoma font works best.
And to be fair without any context even we have difficulty making the difference between an l (lowercase L) and an I (uppercase i) with the font used on this web page. For example the OCR engine needs to distinguish the letter O from the digit 0 and an l from an I. The most challenging for any OCR technology is to distinguish between characters that look similar. OCR stands for Optical Character Recognition, the process to convert an image into searchable / editable computer text and required to extract data automatically from scanned images with a product such as MetaTool or MetaServer.
We often get questions about the best font type to design a header sheet or form that will be used for OCR reading.