Review on Text Extraction in Complex Image using Five Different Web OCR - Dewi Budiwati, Sari; Ananda, Dahliar; Komala Sari, Siska (International Journal of Engineering and Technology, 2019)
Price product comparison is needed for the consumer in order to find the cheapest price. In this research, we build the system to compare the price between several modern markets. The input is product catalogs since consumer often receives it from the modern market. We upload 75 grayscale 75 color input images into five web OCR. We compare the results based on characteristic and segmentation parameter. We define 0.5 and 1 point if web OCR recognizes the product price or/and names from product catalogs. Characteristic parameter is a parameter which identified product price and name using box line or empty line. Meanwhile, for segmentation parameter, we use image properties such as image dimension, dots per inch (dpi), and bit depth. From both parameters, OCR4 gives a better result as it is can recognize 50.67% grayscale image and 62.67% color images. Although it is recognized by OCR4, some of its results shown with data noise. In order to remove the noise, we proposed to used Sparse Binary Polynomial Hashing (SBPH) algorithm with 5-8 letters combination. As the result, some of the text was able to recognize in order to compare its price, while the others with much data noise were not.Â