A Detailed Analysis of Optical Character Recognition Technology

Abstract

Abstract:In many different fields, there is a high demand for storing information to a computer storage disk from the data available in printed or handwritten documents or images to later re-utilize this information by means of computers. One simple way to store information to a computer system from these printed documents could be first to scan the documents and then store them as image files. But to re-utilize this information, it would very difficult to read or query text or other information from these image files. Therefore a technique to automatically retrieve and store information, in particular text, from image files is needed. Optical character recognition is an active research area that attempts to develop a computer system with the ability to extract and process text from images automatically. The objective of OCR is to achieve modification or conversion of any form of text or text-containing documents such as handwritten text, printed or scanned text images, into an editable digital format for deeper and further processing. Therefore, OCR enables a machine to automatically recognize text in such documents. Some major challenges need to be recognized and handled in order to achieve a successful automation. The font characteristics of the characters in paper documents and quality of images are only some of the recent challenges. Due to these challenges, characters sometimes may not be recognized correctly by computer system. In this paper we investigate OCR in four different ways. First we give a detailed overview of the challenges that might emerge in OCR stages. Second, we review the general phases of an OCR system such as pre-processing, segmentation, normalization, feature extraction, classification and post-processing. Then, we highlight developments and main applications and uses of OCR and finally, a brief OCR history are discussed. Therefore, this discussion provides a very comprehensive review of the state-of-the-art of the field.

Dijital Kütüphanelerde Dokümanlardan Bilgi Geri Kazanımı için Kullanılan Güncel Teknolojiler: Derleme Çalışması

“…Optik karakter tanıma (Optical Character Recognition, OCR), taranan belgelerden metinleri elde etmek için kullanılır. OCR, bir sistemin insan müdahalesi olmadan görüntülerden karakterleri veya alfabeleri tanımlamasını sağlayan süreçtir [7,8]. Tipik bir OCR sistemi, analog belgeleri dijitalleştirmek için bir optik tarayıcıdan ve metin içeren bölgeler bulunduğunda sembolleri ayıklamak için bir segmentasyon işlemi bileşenlerinden oluşmaktadır.…”

Test İmtahanlarinin Nəti̇cələri̇ni̇n Avtomati̇k Emali Üçün Kompüter Si̇stemi̇

“…3 , Махмудов Ильгар М. 4 1,3 Институт Систем Управления НАНА, Баку, Азербайджан 2,4 Государственный Экзаменационный Центр Азербайджанской Республики, Баку, Азербайджан 1 kamil_aydazade@rambler.ru, 2 ch.khalilov@dim.gov.az, 3 elshan.mustafayev@gmail.com, 4 i.mahmudov@dim.gov.az Компьютерная система для автоматической обработки результатов тестовых экзаменов Распознавание образов все чаще находит применение в больших информационных системах. Развитие теоретической базы обработки изображений и широкое использование свободных библиотек с открытым кодом делают возможным применение новых решений в разнообразных прикладных задачах.…”

“…Hal-hazırda kütləvi yazılı sorğuların, xüsusən test imtahanlarının nəticələrinin avtomatik emalı üçün istifadə edilən bir çox avtomatlaşdırılmış kompüter sistemlərində Optik Marker Tanıma (OMR) [1][2][3][4] texnologiyaları geniş tətbiq olunur. Bu cür texnologiyalardan istifadə edərək imtahan nəticələrinin emalı qısa müddətdə çox sayda formaların cavablarını obyektiv qiymətləndirməyə və demək olar ki, yüz faiz tanınma dəqiqliyinə nail olmağa imkan verir.…”

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

“…OCR is a process to recognize text from images and videos, and it has relevant applications (Modi and Parikh 2017): vehicle license plate recognition, recognize CAPTCHA, create digital repositories and libraries, recognize invoice imaging and bank statements, extract data from healthcare forms and any suitable documentation. The process of OCR is composited by different steps: image acquisition, preprocessing, character segmentation, feature extraction, character classification and post-processing (Islam et al 2016;Hamad and Kaya 2016). The output of one step is the input of next step.…”