In Chapter 10, Visual and Audio Analysis, we demonstrate several Java techniques for processing sounds and images. We begin by demonstrating techniques for sound processing, including speech recognition and text-to-speech APIs. Specifically, we will use the FreeTTS (http://freetts.sourceforge.net/docs/index.php) API to convert text to speech. We also include a demonstration of the CMU Sphinx toolkit for speech recognition.

The Java Speech API (JSAPI) (http://www.oracle.com/technetwork/java/index-140170.html) supports speech technology. This API, created by third-party vendors, supports speech recognition and speech synthesizers. FreeTTS and Festival (http://www.cstr.ed.ac.uk/projects/festival/) are examples of vendors supporting JSAPI.

In the second part of the chapter, we examine image processing techniques such as facial recognition. This demonstration involves identifying faces within an image and is easy to accomplish using OpenCV (http://opencv.org/).

Also, in Chapter 10, Visual and Audio Analysis, we demonstrate how to extract text from images, a process known as OCR. A common data science problem involves extracting and analyzing text embedded in an image. For example, the information contained in license plate, road signs, and directions can be significant.

In the following example, explained in more detail in Chapter 11, Mathematical and Parallel Techniques for Data Analysis accomplishes OCR using Tess4j (http://tess4j.sourceforge.net/) a Java JNA wrapper for Tesseract OCR API. We perform OCR on an image captured from the Wikipedia article on OCR (https://en.wikipedia.org/wiki/Optical_character_recognition#Applications), shown here:

The ITesseract interface provides numerous OCR methods. The doOCR method takes a file and returns a string containing the words found in the file as shown here:

ITesseract instance = new Tesseract();  
try { 
    String result = instance.doOCR(new File("OCRExample.png")); 
    System.out.println(result); 
} catch (TesseractException e) { 
    System.err.println(e.getMessage()); 
} 

A part of the output is shown next:

OCR engines nave been developed into many lunds oiobiectorlented OCR applicatlons, sucn as reoeipt OCR, involoe OCR, check OCR, legal billing document OCR

They can be used ior

- Data entry ior business documents, e g check, passport, involoe, bank statement and receipt

- Automatic number plate recognnlon

As you can see, there are numerous errors in this example that need to be addressed. We build upon this example in Chapter 11, Mathematical and Parallel Techniques for Data Analysis, with a discussion of enhancements and considerations to ensure the OCR process is as effective as possible.

We will conclude the chapter with a discussion of NeurophStudio, a neural network Java-based editor, to classify images and perform image recognition. We train a neural network to recognize and classify faces in this section.