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Classification is used to distinguish multiple objects from each other. Image classification is an essential and challenging task in various application domains.Machines use their senses to do things like planning,pattern recognizing,understanding natural language, learning and solving problems. And Image Recognition is one of its senses!!!

Classification process consists of the following:

  • Image Acquisition: This consists of acquiring the images for image classification.

  • Pre-processing: A number of preprocessing steps may be required based on the quality of image like image correction, noise removal, image transformation, main component analysis, etc.

  • Detection and extraction of object of interest: Detection includes detection of the position of an object in the image while in extraction the object along with its essential features is extracted from the image.

  • Training: This includes the selection of particular attributes of the object which best describes the specific patterns in the images.

  • Classification of the object: Object classification step categorizes detected objects into one of the predefined classes by using suitable method that compares the image patterns with the target patterns.


Some of the challenges faced by the classification of images are:

  • Alignment Variation: Entities in the images are not always aligned when the images are fed into the system for further processing. This results in inaccurate outputs. The system cannot identify the misalignment of the image from left, right, bottom or top.

  • Signal to Error Ratio: An image may be affected by noise either during acquisition process or during transmission. Identifying the type and degree of noise introduces unwanted sections in the image increasing the signal to error ration. As a result of which image processing models generate unpredicted results.

  • Scale Variation: Extracting specified objects of interest from image are affected by the size of the image. The same image as looked from a closer eye presents the object to be bigger than its actual size. It makes challenging to pick minute features from the images which are too small.

  • Deformation: Deformed images fail to generate accurate results.

  • Obstruction: Sometimes, while capturing the images intended for classification or recognition suffer from occlusion. This happens when unwanted objects obstruct the complete view of the image.

Use Cases

Image classification usually depends on digital image processing. Image classification is applied in many areas such as medical imaging, object identification, control systems, industrial visual inspection, machine vision, etc. Here are some use cases as in why businesses need image classification.

  • Invoice Recognition

    Manually sorting the invoices received from many vendors is tasking especially when the daily inflow of invoices is high. The automated image classification system can recognize the invoices by the name of the vendor.

  • Information Extraction

    Sorted invoices make it easy to extract the desired information like vendor name, invoice number, invoice date, line items and invoice total from the invoice.