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Handwritten Digit Recognition Using Artificial Intelligence on a Low-Cost FPGA Board

In a computer vision system, handwritten digits recognition is a complex task that is central to a variety of emerging applications. It has been widely used by machine learning and computer vision researchers for implementing practical applications like computerized bank check numbers reading. Today the recognition of handwritten digits using different AI-techniques is also famous in academia for teaching and learning purposes.

Recognition of Handwritten Digits is Complex

While the presentation of the problem itself has not changed over the years, computing power has increased dramatically during recent decades, making it now possible to run the recognition on an FPGA (Field Programmable Gate Array) board suitable for teaching, together with a small digital camera. Nevertheless, it is still one of the harder tasks for artificial intelligence to tackle.

Implementing a Textbook Version

But how does the recognition work? Looking at it from a very high-level, it’s straightforward: An image of the digit needs to be captured, processed and analyzed, and the result presented to the user.

Digilent, for example, implemented a textbook-version of the digit recognition through artificial intelligence as a proof-of-concept using their 5 megapixels Pcam 5C  (174-1555) fixed-focus color camera module for the image capture, their Zybo Z7 Xilinx Zynq-7000 ARM/FPGA  (164-3486) development board, and its Pmod MTDS multi-touch display system as user interface and to display the result of the recognition process (the whole setup is available at a discount in our Embedded Vision Bundle (175-2109) .


What was achieved?

This textbook-implementation of handwritten digit recognition using a low-cost FPGA-board demonstrated that is it possible to implement such an artificial neural network with deep learning on such a system. Once trained, the recognition rate of the trained artificial neural network on the MNIST test images reached 95.2%.

The Xilinx Zynq-7000 ARM/FPGA (164-3486) development board, together with the Pcam 5C (174-1555) and the Pmod MTDS display – all from Digilent and available at a discount in the Embedded Vision Bundle (175-2109) – allows you to simply take a textbook and to implement a live capture and inference system from scratch.

Read more about the project and all the steps on the Digilent Blog.

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