Enhanced Numeral Recognition for Handwritten Multi-language Numerals Using Fuzzy Set-Based Decision Mechanism

Abstract

Handwritten character and numeral recognition have gained interest in the research community as part of the big picture of Machine Learning. Writer independent recognition systems are still in the working and the research is geared towards an optimized technique that can achieve this. In this paper, we propose a numeral recognition system that forms fuzzy sets of the features extracted using modified structural features for English, Arabic, Persian, and Devanagari Numerals. The structural features extract the geometrical primitives that distinguish each image. After the feature extraction phase, the results are input into a classifier, we test two different classifiers namely Neural Network and Naïve Base. To further enhance the recognition process with low overhead the erroneously recognized numerals (confusion matrix) are processed through the fuzzy set-based decision mechanism to enhance the numeral recognition process. Results indicate that recognition is enhanced by applying the fuzzy set-based decision mechanism for both classifer.

Feature Extraction in Six Blocks to Detect and Recognize English Numbers

“…There are many difficulties in making a machine do simple activity like recognize numerals in question with handwritten. The linear model of conventional computing is confronted by differences in writing style, accuracy, alignment, and stray marks in the vicinity of a number, etc., which highlights the need for a computing system that can handle data more in the way the human brain does [1]. In this study, a handwritten numeral (HN) image is converted by using the threshold method to a binary image.…”

“…Properties ranges are vital in the coded stage and, especially, in the training step. Properties ranges are obvious in Table (1) which shows the high, medium, or low range depending on its value in figure 6. The number sequence, in figure 7, starts from 0 at the bottom and ends with 9 at the top, as shown with the Arial font type.…”

“…The method of pattern recognition is known to recognized an input pattern information, therefore the output data represent the classes of the image [1]. Character recognition is one of the essential areas of pattern recognition.…”

Handwritten Arabic Digit Recognition Using Convolutional Neural Network

“…Their system was evaluated on the ADBase dataset with an accuracy of 88%. Al-Hmouz et al [9] proposed a digit numeral recognition system for Arabic, English, Devanagari, and Persian numbers. Structural features were extracted using the image geometrical primitives.…”

KurdSet Handwritten Digits Recognition Based on Different Convolutional Neural Networks Models

“…This technology is now mature and highly accurate [3]. In another study [4], researchers developed a system for recognizing digit numerals in Arabic, English, Devanagari, and Persian languages. The system extracted structural characteristics of the numerals utilizing neural network and naïve Bayes classifiers on geometric primitives within images, resulting in high levels of accuracy.…”