Recognition

Auto Clicker with Image Recognition

Recognition is the part that decides everything: before tapping, the automation has to answer whether the target is on screen and where. Emulo answers that three ways, by image, text and color, with the same engine on the phone and on the PC.

Crop matching with adjustable similarity
Text reading with exact, partial or pattern search
Color conditions by pixel or by region
Occurrence counting and search regions

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The problem

Finding before clicking

Every auto clicker has to answer the same question: where to tap. A coordinate clicker answers it at recording time and repeats that answer forever. A clicker with recognition postpones the answer to run time, and that is what makes it able to deal with a screen that changes.

The trade-off is that recognition costs processing and can be wrong. A good engine is not the one that promises to always be right, but the one that gives you control over the error margin and avoids false hits. The sections below show how each of the three kinds of recognition works and where each one fits best.

Image

Crop matching

You crop a piece of the screen, the target, and the engine looks for that piece inside the current capture. The result is a similarity score and the position where it was found. If the score clears the configured minimum, the condition is met and the tap is computed from the center of that position.

The delicate part is the minimum. Too loose and anything similar counts as a hit. Too strict and a small difference in compression or brightness kills the detection. That is why the engine does not treat every crop the same.

Minimum similarity: Adjustable, starting at 0.85. It is the passing grade: below it, the engine considers the target not found.
Automatic floor: Small crops produce accidental coincidences more easily. When the target is small, the engine raises the passing grade on its own, reaching 0.95 in the most critical cases, and warns you when the requested value is too low to be trusted.
Color check: After a match, the engine compares the average color of the region against the crop. This is what rules out the classic false positive, where the structure matches but the color is completely different.
Search region: You can limit the search to a rectangle of the screen. It cuts the cost and, more importantly, eliminates hits in places where the target should not be.
Occurrence counting: Instead of only asking whether it appeared, you can ask how many times it appeared and compare against a number. The same scan feeds the action that clicks every occurrence found.

Text reading

Text reading identifies what is written on screen and returns the lines it found. The condition checks whether the word or phrase you defined is present, and the matching action taps the center of the located text, with no position for you to mark.

It is the right pick when the target is a label. Buttons change color, glow, animate or change style between versions, but the text stays the same.

Ways to compare: Contains, equals, differs, or matches a pattern. The pattern covers cases like a number that varies inside a fixed format.
Case and accents: The comparison normalizes both sides before matching, so differences in accents and capitalization do not get in the way.
Minimum confidence: Every line read carries a confidence score, and you set the value from which it should be considered.
Cost: It is the most expensive of the three. It usually pays off behind a cheap condition, such as a color check, that only lets the text be read when it makes sense.

Color conditions

The color check compares pixel values against the color you defined, within a tolerance. It can look at a specific pixel or scan a whole rectangle for any pixel that matches.

It covers what has no stable shape. A health bar is not a fixed image, it is a strip that shrinks. An on and off indicator sometimes changes only in color. A screen that went dark is simply dark.

Pixel or region: An exact point, when the position is reliable, or a rectangle, when it is enough for the color to show up somewhere inside it.
Tolerance: Set per color channel, to absorb variations in compression, brightness and edge smoothing without giving up the check.
Use as a filter: It is the cheapest of the three checks, which makes it useful as the first test in a step, leaving heavier searches for when it passes.

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Which one

Choosing recognition by the kind of target

There is no best one in the abstract. There is the one that suits what you are trying to find.

	Best recognition	Why
Icon or button with a fixed drawing	Image	The shape is stable and the crop is precise, at low cost.
Button with a written label	Text	It survives changes in color, brightness, animation and style.
A number that changes	Text	Pattern matching covers the variation inside a fixed format.
Health or energy bar	Color	It has no stable shape: what changes is how much is filled.
On and off indicator	Color	Often the only difference between the two states is the color.
Loading or blank screen	Color	A cheap check settles it, without scanning the whole screen.
How many identical items	Image, with counting	The scan finds every occurrence and compares against a number.
Keypad with shuffled digits	Image, before each tap	The position changes on every open, so each key is located right before being tapped.

Nothing stops you from combining more than one kind of recognition in the same step. Using color as a fast filter and image to confirm is common.

Combining

Recognition becomes automation when the pieces come together

On its own, a detection only answers yes or no. What turns that into reliable automation is how the conditions combine.

AND and OR conditions

A step can require several conditions at once, or accept any one of them. Groups can be nested, which covers situations like "if this is the screen and the button is active, or if the error notice showed up".

Negative conditions

Checking that something is not on screen is as useful as the opposite. That is how you wait for a load to finish or confirm that a notice is gone.

Search region

Restricting the search area cuts the cost of each check and keeps the target from being found somewhere it should not be.

Keypads with shuffled digits

Keypads that move their digits on every open break any blind automation. Here each key is located immediately before being tapped, and execution stops if there is any doubt about which digit is being seen.

Counting and variables

The result of a count can become a decision, and variables hold state between steps, letting you repeat something a set number of times or change the course of the flow.

The same engine in both apps

The three detections, the condition combinations and the virtual keyboard exist in both EmuloMobile and EmuloAgent. What changes is where the capture happens, not how recognition decides.

Questions

Frequently asked questions

What happens if the image is not found?

The condition is not met and the step does not run. There is no consolation click at an approximate position. You decide what happens then: wait, take another path, or stop.

Is image recognition slow?

A crop comparison is fast, and a color check is cheaper still. Text reading is the most expensive of the three. In practice, what defines performance is how the automation is designed: limiting the search region and putting a cheap condition ahead of an expensive one makes more difference than any isolated setting.

How do I avoid false positives?

Three things help: picking crops with enough detail instead of tiny generic pieces, limiting the search region, and keeping the minimum similarity high. The engine also checks the color of the matched region, which rules out a good share of misleading hits.

Do I need to redo the crops if the resolution changes?

Crops are calibrated for the resolution they were made at. When a different resolution is detected, the app warns you instead of carrying on blindly, and keeps the crops from previous resolutions stored.

Does it work with animations and effects?

It depends on what changes. If the element glows or moves slightly, tolerance usually handles it. If it changes shape entirely, the way forward is to recognize it by the label text or by a characteristic color.

Can I combine recognition with coordinate clicking?

Yes. Tapping by coordinate is still available as an action and is useful when the position really is fixed. The difference is that it becomes a deliberate choice inside a step with a condition, rather than the only way to act.

Keep reading

Other pages that go deeper on topics related to this one.

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