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PICTURE TEXT RECOGNITION SOFTWARE FOR CHINESE FREE DOWNLOAD 720P
PICTURE TEXT RECOGNITION SOFTWARE FOR CHINESE FREE DOWNLOAD CODE

text_detection.py : Detects text in static images.

You may wish to add your own images collected with your smartphone or ones you find online. Notice that I’ve provided three sample pictures in the images/ directory. From there, simply use the tree terminal command to view the project structure: $ tree -dirsfirst

PICTURE TEXT RECOGNITION SOFTWARE FOR CHINESE FREE DOWNLOAD CODE

To start, be sure to grab the source code + images to today’s post by visiting the “Downloads” section. To build and train such a deep learning model, the EAST method utilizes novel, carefully designed loss functions.įor more details on EAST, including architecture design and training methods, be sure to refer to the publication by the authors. Perhaps most importantly, since the deep learning model is end-to-end, it is possible to sidestep computationally expensive sub-algorithms that other text detectors typically apply, including candidate aggregation and word partitioning.

PICTURE TEXT RECOGNITION SOFTWARE FOR CHINESE FREE DOWNLOAD 720P

The EAST pipeline is capable of predicting words and lines of text at arbitrary orientations on 720p images, and furthermore, can run at 13 FPS, according to the authors. We call the algorithm “EAST” because it’s an: Efficient and Accurate Scene Text detection pipeline. With the release of OpenCV 3.4.2 and OpenCV 4, we can now use a deep learning-based text detector called EAST, which is based on Zhou et al.’s 2017 paper, EAST: An Efficient and Accurate Scene Text Detector. Unknown layout: We cannot use any a priori information to give our algorithms “clues” as to where the text resides.Īs we’ll learn, OpenCV’s text detector implementation of EAST is quite robust, capable of localizing text even when it’s blurred, reflective, or partially obscured:įigure 3: The structure of the EAST text detection Fully Convolutional Network (Figure 3 of Zhou et al.).We need to be able to handle such use cases. While humans may still be able to easily “detect” and read the text, our algorithms will struggle. Non-planar objects: Consider what happens when you wrap text around a bottle - the text on the surface becomes distorted and deformed.

Text in natural scenes may be reflective, including logos, signs, etc.

Non-paper objects: Most, but not all, paper is not reflective (at least in context of paper you are trying to scan).

Resolution: Not all cameras are created equal - we may be dealing with cameras with sub-par resolution.

It may be near dark, the flash on the camera may be on, or the sun may be shining brightly, saturating the entire image.

Lighting conditions: We cannot make any assumptions regarding our lighting conditions in natural scene images.

Blurring: Uncontrolled environments tend to have blur, especially if the end user is utilizing a smartphone that does not have some form of stabilization.

Viewing angles: Natural scene text can naturally have viewing angles that are not parallel to the text, making the text harder to recognize.

Additionally, low-priced cameras will typically interpolate the pixels of raw sensors to produce real colors.

Image/sensor noise: Sensor noise from a handheld camera is typically higher than that of a traditional scanner.

I’ve included a summarized version of the natural scene text detection challenges described by Celine Mancas-Thillou and Bernard Gosselin in their excellent 2017 paper, Natural Scene Text Understanding below: Natural scene text detection is different though - and much more challenging.ĭue to the proliferation of cheap digital cameras, and not to mention the fact that nearly every smartphone now has a camera, we need to be highly concerned with the conditions the image was captured under - and furthermore, what assumptions we can and cannot make. An example of such a heuristic-based text detector can be seen in my previous blog post on Detecting machine-readable zones in passport images. Figure 1: Examples of natural scene images where text detection is challenging due to lighting conditions, image quality, and non-planar objects (Figure 1 of Mancas-Thillou and Gosselin).ĭetecting text in constrained, controlled environments can typically be accomplished by using heuristic-based approaches, such as exploiting gradient information or the fact that text is typically grouped into paragraphs and characters appear on a straight line.