Enhanced Handwritten Document Recognition Using Confusion Matrix Analysis

The handwritten Multilanguage phase is the preprocessing phase that improves the image quality for better identification in the system. The main goals of preprocessing are diodes, noise suppression and line cancellation. After word processing, various attribute extraction techniques are used to process attribute properties for the identification process. Smoothing plays an important role in character recognition. The partitioning process in the word distribution strategy can be divided into global and local texts. The writer does not use this header line to write the text which creates a problem for skew correction, classification and recognition. The dataset used are HWSC and TST1. The tensor flow method is used to estimate the consistency of confusion matrix for the enhancement of the text recognition .The accuracy of the proposed method is 98%.

Skew correction for mushaf Al-Quran: a review

Skew correction have been studied a lot recently. However, the content of skew correction in these studies is considered less for Arabic scripts compared to other languages. Different scripts of Arabic language are used by people. Mushaf A-Quran is the book of Allah swt and used by many people around the world. Therefore, skew correction of the pages in Mushaf Al-Quran need to be studied carefully. However, during the process of scanning the pages of Mushaf Al-Quran and due to some other factors, skewed images are produced which will affect the holiness of the Mushaf Al-Quran. However, a major difficulty is the process of detecting the skew and correcting it within the page. Therefore, this paper aims to view the most used skew correction techniques for different scripts as cited in the literature. The findings can be used as a basis for researchers who are interested in image processing, image analysis, and computer vision.

Handwritten Kannada Numerals Recognition by Using Zone Features and CNN Classifier

The proposed system presents a pre-processing, segmentation, features extraction approach and Deep Convolutional Neural Network (DCNN) classifier for recognition of handwritten Kannada numerals. Pre-processing have different steps like median filter, gray scale to binary, normalization, thinning, skew correction and slant removal. Segmentation process contains different methods like vertical projection profile for word and novel character segmentation. Collections of best discriminable features are very important part in achieving high rate of identification in automatic numeral detection systems. Kannada is the major south Indian character verbal by about 50 million people. This article presents a well-organized and novel technique for recognition of handwritten Kannada numerals using zone and distance matrix. An appropriate feature extractor and a superior classifier play most important task in achieving high detection rate for a recognition scheme. This article determines a variety of feature extraction approaches and classification techniques which are designed to recognize handwritten numerals of Kannada script. The DCNN classifier approach is used to classify the testing samples of each Kannada handwritten numerals. The experimental result gives the acceptable performance rate.

An Experimental Technique for OCR Line and Word Segmentation using Probability Distribution Estimation

Segmentation is always an important step in designing an Optical Character Recognition (OCR) of any script. In this paper, we focus on the line and word segmentation in typewritten Gurmukhi script documents. In order to perform this task, we consider OCR based methodology where several processing steps are implemented. The typewritten documents suffer from several issues such as noise, skew, and quality of the document. In this work, we present a combined pre-processing scheme where document thresholding and skew detection and correction schemes are implemented where image thresholding is obtained using Niblack’s method and skew correction is carried out using gradient histogram algorithm and uniform orientation is obtained. Later, line segmentation scheme is applied where probability density function is applied to generate the text distribution in the probability map. Here, identifying the relation of the text to the exact line is a challenging task hence, we present a 2D-Gaussian modelling which helps to identify the text boundaries in the x and y direction. The proposed methodology is applied for typewritten Gurmukhi documents and an experimental study is carried out to show that the proposed approach achieves better performance when compared with the existing techniques