M. C. Parikh

Bio: M. C. Parikh is an academic researcher. The author has contributed to research in topics: Artificial intelligence & License. The author has an hindex of 1, co-authored 1 publications receiving 22 citations.

A Review on Optical Character Recognition Techniques

Abstract: At present scenario, there is growing demand for the software system to recognize characters in a computer system when information is scanned through paper documents. This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determined that have been proposed to realize the center of character recognition in an optical character recognition system. OCR (Optical Character Recognition) translates images of typewritten or handwritten characters into the electronically editable format and it preserves font properties. Different techniques for preprocessing and segmentation have been surveyed and discussed in this paper.

Various License Plate Detection and Recognition Methods using Computer Vision and Machine Learning

Abstract: With the increasing advancements in the technology, our lives have become significantly more convenient. We now have automated many things. One example of such things is the automated number plate recognition system. There are many ways to perform the ANPR (Automatic Number Plate Recognition). Performing ANPR in wild still remains a big challenge. This review focuses on some techniques that have tried to overcome this challenge.

Character and numeral recognition for non-Indic and Indic scripts: a survey

Abstract: A collection of different scripts is employed in writing languages throughout the world. Character and numeral recognition of a particular script is a key area in the field of pattern recognition. In this paper, we have presented a comprehensive survey on character and numeral recognition of non-Indic and Indic scripts. Many researchers have done work on character and numeral recognition from the most recent couple of years. In perspective of this, few strategies for character/numeral have been developed so far. There are an immense number of frameworks available for printed and handwritten character recognition for non-Indic scripts. But, only a limited number of systems are offered for character/numeral recognition of Indic scripts. However, few endeavors have been made on the recognition of Bangla, Devanagari, Gurmukhi, Kannada, Oriya and Tamil scripts. In this paper, we have additionally examined major challenges/issues for character/numeral recognition. The efforts in two directions (non-Indic and Indic scripts) are reflected in this paper. When compared with non-Indic scripts, the research on character recognition of Indic scripts has not achieved that perfection yet. The techniques used for recognition of non-Indic scripts may be used for recognition of Indic scripts (printed/handwritten text) and vice versa to improve the recognition rates. It is also noticed that the research in this field is quietly thin and still more research is to be done, particularly in the case of handwritten Indic scripts documents.

Industrial Optical Character Recognition System in Printing Quality Control of Hot-Rolled Coils Identification

Abstract: This work presents a system designed to detect printing errors and misidentifications on steel coils that could lead to tracking problems and even guide to the delivery of the wrong product to the final client. An optical character recognition system is proposed to extract the printed identification of steel coils from images captured by a fixed camera in an industrial environment. The method considers different digital image processing techniques to deal with the significant lighting and printing variation observed, followed by a segmentation process that extracts and aligns the characters originally printed in an arch form, ending with a classification routine based on a convolutional neural network. The proposed system presents an approach to treat lighting variations in images, covering low contrast, darker and brighter images. Experiment carried out on a data set with approximately 20,000 images achieved an accuracy higher than 98%, supporting the validity of the proposed method.

Document Segmentation and Language Translation Using Tesseract-OCR

Abstract: Document segmentation and Translation are one of the key areas in pattern recognition and natural language processing. This paper presents details about translation in terms of a web application that accepts image document as an input, where input document is a user define image file containing text in any language available in the Python-tesseract library and does its exact translation in any supported languages using Google Translator (i.e Googletrans). Python script and various libraries are used to approach various challenges in segmentation and translation of a document.

Efficient Gabor-Based Recognition for Handwritten Arabic-Indic Digits

Abstract: In daily life, the need of automatically digitizing paper documentations and recognizing textual images is still present with existing and potential upcoming rooms for improvements, especially for languages like Arabic, which is unlike English as an instance, has more complex context and not been extensively supported by research in a such domain. As yet, the available online offline optical character recognition (OCR) systems have utilized functional techniques and achieved high performance mainly on machine printed data images. However, in case of handwritten script, the recognition task becomes highly unconstrained and much more challenging. Amongst a large verity of recognizable multi-lingual characters, handwritten digit recognition is a considerably useful task for different purposes and countless applications. In this research, the focus is on Arabic (known today as Indic or Indian) digit recognition using different proposed Gabor-based approaches in several combinations with different classification methods. The proposed approaches are trained and tested using 91120 digit samples of two independent standard databases (Arabic-Handwritten-Digits and AHDBase), allowing performance variability assessments and comparisons not only between the different combinations of features and classifiers but also between different datasets. The proposed Arabic-Indic digit recognition system achieves high recognition rates reach up to 99.87%. This research practically shows that one of the proposed approaches with significant dimensionality reduced features remains attaining a high recognition rate with low complexity time, which can be hence recommended further for online digit recognition systems.

Segmentation of Touching Arabic Characters in Handwritten Documents by Overlapping Set Theory and Contour Tracing

Abstract: Segmentation of handwritten words into characters is one of the challenging problem in the field of OCR. In presence of touching characters, make this problem more difficult and challenging. There are many obstacles/challenges in segmentation of touching Arabic handwritten text. Although researches are busy in solving the problem of segmentation of these touching characters but still there exist unsolved problems of segmentation of touching offline Arabic handwritten characters. This is due to large variety of characters and their shapes. So in this research, a new method for segmentation of touching Arabic Handwritten character has been developed. The main idea of the proposed method is to segment the touching characters by identifying the touching point by overlapping set theory and ending points of the Arabic word by applying some standard morphology operation methods. After identifying all the points, segmentation method is applied to trace the boundaries of characters to separate these touching characters. Experiments were conducted on touching characters taken from different data sets. The results show the accuracy of the proposed method.