I'm pretty impressed with Microsoft's System.Speech API. It took less than 3 days to throw together a proof-of-concept application. The hardest part was probably coming up with the grammar -- documentation for that is pretty thin on the ground.
Not wanting to hassle with learning OpenCV and fighting with an edit-compile-execute environment, I decided to use my OpenCV project as an excuse to play around with Python.
I'm still a serious beginner, but I'm beginning to understand why it gets the use it does.
Anyhow, it only took a couple of days to integrate Tesseract OCR, PIL, and OpenCV such that I could open multi-frame TIFF images, perform some basic feature detection, and then use the output of feature detection to focus on a specific region for OCR.
I will admit to having a few false starts. The first was that I used an older (C++) tutorial that was using some deprecated features of OpenCV and ignoring some other features. For example, the tutorial was using Hough Line detection to find squares on a printed page. In order to get to that point there was thresholding, dilating, eroding, inversion, flood filling and so on. Even then I wasn't getting the correct results.
As much as I've dogged on Python in the past (significant whitespace, really?), I've got to admit that it's
got some cool features too.
For example, I'm playing with libpuzzle (a library for visually comparing images). It has a command line utility and a C and PHP API. Unfortunately, the CLI utility doesn't allow one to dump the raw comparison vector, and it's a PITA to write C just to play with a library.
Python's native "ctypes" to the rescue!
from ctypes import *
_fields_ = [("sizeof_vec", c_size_t),
_fields_ = [("sizeof_compressed_vec", c_size_t),
_fields_ = [("puzzle_max_width", c_uint),