Automatic optical inspection for detecting keycaps misplacement using Tesseract optical character recognition

This research study aims to develop automatic optical inspection (AOI) for detecting keycaps misplacement on the keyboard. The AOI hardware has been designed using an industrial camera with an additional mechanical jig and lighting system. Optical character recognition (OCR) using the Tesseract OCR engine is the proposed method to detect keycaps misplacement. In addition, captured images were cropped using a predefined region of interest (ROI) during the setup. Subsequently, the cropped ROIs were processed to acquire binary images. Furthermore, Tesseract processed these binary images to recognize the text on the keycaps. Keycaps misplacement could be identified by comparing the predicted text with the actual text on the golden sample. Experiments on 25 defects and 25 non-defected samples provided a classification accuracy of 97.34%, a precision of 100%, and a recall of 90.70%. Meanwhile, the character error rate (CER) obtained from the test on a total of 57 characters provided a performance of 10.53%. This outcome has implications for developing AOI for various keyboard products. In addition, the precision level of 100% signifies that the proposed method always offers correct results in detecting product defects. Such outcomes are critical in industrial applications to prevent defective products from circulating in the market

Unicode-driven Deep Learning Handwritten Telugu-to-English Character Recognition and Translation System

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively

Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques

The potential uses of cursive optical character recognition, commonly known as OCR, in a number of industries, particularly document digitization, archiving, even language preservation, have attracted a lot of interest lately. In the framework of optical character recognition (OCR), the goal of this research is to provide a thorough understanding of both cutting-edge methods and the unique difficulties presented by Indic scripts. A thorough literature search was conducted in order to conduct this study, during which time relevant publications, conference proceedings, and scientific files were looked for up to the year 2023. As a consequence of the inclusion criteria that were developed to concentrate on studies only addressing Handwritten OCR on Indic scripts, 53 research publications were chosen as the process's outcome. The review provides a thorough analysis of the methodology and approaches employed in the chosen study. Deep neural networks, conventional feature-based methods, machine learning techniques, and hybrid systems have all been investigated as viable answers to the problem of effectively deciphering Indian scripts, because they are famously challenging to write. To operate, these systems require pre-processing techniques, segmentation schemes, and language models. The outcomes of this methodical examination demonstrate that despite the fact that Hand Scanning for Indic script has advanced significantly, room still exists for advancement. Future research could focus on developing trustworthy models that can handle a range of writing styles and enhance accuracy using less-studied Indic scripts. This profession may advance with the creation of collected datasets and defined standards