# Image Adaptive Threshold Apply adaptive threshold operation that calculates threshold for small regions, suitable for varying lighting conditions > Canonical page: https://elysiatools.com/en/tools/image-adaptive-threshold - **Category:** Design - **Keywords:** image, adaptive, threshold, local, binary, lighting, region, binarization ## Overview The Image Adaptive Threshold tool performs local binarization on images, calculating unique thresholds for small pixel neighborhoods to effectively handle uneven lighting and shadows. ## Inputs - **Image File** (file): Select an image file... - **Block Size (odd number)** (number): Size of a pixel neighborhood that is used to calculate a threshold for the pixel (must be odd) - **Constant** (number): Constant subtracted from the mean or weighted mean. Usually positive. - **Adaptive Method** (select): Adaptive thresholding method to use ## When to use - When an image has inconsistent lighting that makes global thresholding ineffective. - When you need to extract text or fine details from documents with shadows or gradients. - When preparing images for OCR or feature detection where local contrast is more important than overall brightness. ## How it works - Upload your image file in a supported format like PNG, JPEG, or TIFF. - Set the block size to define the neighborhood area for local threshold calculation. - Choose between Gaussian or Mean methods to determine how the threshold is weighted within each block. - Apply the constant value to fine-tune the sensitivity of the binarization process. ## Use cases - Binarizing scanned documents with uneven page lighting or shadows. - Isolating objects from backgrounds in images with complex, non-uniform illumination. - Preprocessing images to improve the accuracy of optical character recognition (OCR) software. ## Frequently asked questions ### What is the difference between Gaussian and Mean methods? The Mean method calculates the average of the neighborhood, while the Gaussian method uses a weighted sum based on a Gaussian distribution, often resulting in smoother edges. ### Why must the block size be an odd number? An odd block size ensures there is a clear center pixel for the neighborhood calculation, which is required for the mathematical operation. ### What does the constant value do? The constant is subtracted from the calculated mean; increasing it can help remove background noise or faint artifacts from the final binary image. ### Which file formats are supported? The tool supports common image formats including JPEG, PNG, WebP, GIF, BMP, and TIFF. ### Can I process multiple images at once? No, this tool is designed to process one image file at a time. ## Related tools - [Image Box Blur](https://elysiatools.com/en/tools/image-box-blur): Apply box blur kernel for simple uniform averaging and fast noise reduction - [Image Boolean AND](https://elysiatools.com/en/tools/image-boolean-and): Apply bitwise AND operation between two images to create intersection mask - [Image Boolean NOT](https://elysiatools.com/en/tools/image-boolean-not): Apply bitwise NOT operation to invert all pixel values in an image - [Image Boolean OR](https://elysiatools.com/en/tools/image-boolean-or): Apply bitwise OR operation between two images to create union mask - [Image Boolean XOR](https://elysiatools.com/en/tools/image-boolean-xor): Apply bitwise XOR operation between two images to create difference mask - [Image Noise Reduction](https://elysiatools.com/en/tools/image-noise-reduction): Reduce image noise using multiple algorithms including median, gaussian, and bilateral filtering - [Bilateral Filter](https://elysiatools.com/en/tools/image-bilateral-filter): Apply bilateral filter for edge-preserving smoothing that reduces noise while maintaining image details - [CLAHE Histogram Equalization](https://elysiatools.com/en/tools/image-clahe): Apply Contrast Limited Adaptive Histogram Equalization for local contrast enhancement ## Samples - [Web Image Processing Python Samples](https://elysiatools.com/en/samples/web-image-processing-python): Web Python image processing examples using PIL/Pillow including reading, saving, resizing, and format conversion - [Web Image Processing TypeScript Samples](https://elysiatools.com/en/samples/web-image-processing-typescript): Web TypeScript image processing examples including image read/save, scaling, and format conversion - [Android Image Processing Java Samples](https://elysiatools.com/en/samples/android-image-processing-java): Android Java image processing examples including reading/saving images, scaling, and format conversion - [Android Image Processing Kotlin Samples](https://elysiatools.com/en/samples/android-image-processing-kotlin): Android Kotlin image processing examples including reading/saving images, scaling, and format conversion ## Related content - [Binary Image Thresholding, Morphology, and Mask Tools](https://elysiatools.com/en/hubs/binary-image-thresholding-and-morphology): Turn images into binary masks, compare threshold methods, repair foreground shapes, and combine masks with morphological and Boolean operations.