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Optical Character Recognition (OCR) in the scanned documents has been a well-studied problem in the past. However, when these characters come from the natural scenes, it becomes a much more challenging problem, as there exist many difficulties in these images, e.g., illumination variance, cluttered backgrounds, geometry distortion. In this paper, we propose to use a deep learning method that based...
Scene Text Recognition is an extremely useful but challenging task and has drawn much attention in recent years. The best of previous model is CNN-LSTM model with attention mechanism, and it can recognize the whole text without character-level segmentation and recognition. Compared with LSTM, Recurrent Highway Networks (RHN), as a popular architecture because of its capability of training deep structure,...
In modern time, finger-vein recognition technology has become increasingly popular. Basically, the finger-vein recognition process involves finger-vein image acquisition, feature extraction and recognition. The recognition algorithm is the key research issue. Because of the differences between acquisition devices and individuals, the performance of the algorithm is affected by image rotation, translation...
Building a modern Optical Character Recognition (OCR) system for Chinese is hard due to the large Chinese vocabulary list. Training images for rare Chinese characters are extremely expensive to obtain. Radical-based OCR systems tackle this problem by first extracting and recognizing basic graphical components (i.e., radicals) of a Chinese character. However, how to reliably recognize radicals still...
This article describes a method for recognizing Ukrainian car's license plates of the most common format, which has a high percentage of correct recognition and applied in practice.
Scene text recognition has been a hot research topic in computer vision due to its various applications. The state of the art is the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. However, we observe that existing attention-based methods perform poorly on complicated and/or low-quality images. One major reason...
The article looks at the current state of the mobile apps market and its potential to generate solutions for B2C and B2B segments. It also explores the issue of how mobile devices can be made capable of recognizing printed text containing images. Technologies are described for mobile application implementation. The article further explains how signal rank processing algorithm can be used to improve...
Automatic License Plate Recognition (ALPR) has been employed in many developed countries for traffic management, automatic speed control, tracking stolen cars and also in automatic toll systems for improving the traffic control. ALPR is a surveillance system that extracts the information from the vehicle license plate by capturing the images. Human intervention to recognize the license plates results...
This work is focused on recognition of license plates in low resolution and low quality images. We present a methodology for collection of real world (non-synthetic) dataset of low quality license plate images with ground truth transcriptions. Our approach to the license plate recognition is based on a Convolutional Neural Network which holistically processes the whole image, avoiding segmentation...
The aim is to develop an efficient method which uses a custom image to train the classifier. This OCR extract distinct features from the input image for classifying its contents as characters specifically letters and digits. Input to the system is digital images containing the patterns to be classified. The analysis and recognition of the patterns in images are becoming more complex, yet easy with...
At present, most medical sheet (such as medical report, laboratory sheet, medical cases, etc.) in the form of nonelectronic is easy to lose, and difficult to be integrated with other electronic health data. In order to fully utilize these valuable data, in this paper we propose a deep learning approach, named k-CNN, which can intelligently recognize the contents of medical sheet. The main advantages...
Once we had tried to propose an unbreakable CAPTCHA and we reached a result that limitation of time is effect to prevent computers from recognizing characters accurately while computers can finally recognize all text-based CAPTCHA in unlimited time. One of the existing usual ways to prevent computers from recognizing characters is distortion, and adding noise is also effective for the prevention....
This paper describes a web-based system for page segmentation and text recognition of historical documents. The system is organised following a pipeline of 4 steps : 1) digitisation, 2) preprocessing, 3) textline extraction, and 4) handwritten text recognition based on hidden Markov models. In this study we used to evaluate the system the “Statuti del Doge Tiepolo”, a 14th century manuscript written...
Text in natural scenes provides many information for peoples and presents an essential tool to interact with their environment. Therefore, recognizing text existing in camera-captured images has become an important issue for many researches in the last decades. Currently, there isn't any available dataset of Arabic script text images in the wild. Since our aim is to help the research community in...
In this paper, a font size independent Optical Character Recognition (OCR) system for Urdu document images is presented. Urdu documents are written using Noori Nastalique writing style with different font sizes of normal text and headings. Most of current state of the art techniques of Urdu OCRs support recognition of text having single font size. The presented study deals with the recognition of...
The authors have conducted studies on Arabic news caption recognition to develop a system for video retrieval by keyword to index and edit Arabic broadcast programs received daily and stored in a big database. This paper proposes a dedicated OCR for recognizing low resolution news caption in video images. The news caption recognition system consisting of text line extraction, word segmentation and...
The technological advancement and sophistication in cameras and gadgets prompt researchers to have focus on image analysis and text understanding. The deep learning techniques demonstrated well to assess the potential for classifying text from natural scene images as reported in recent years. There are variety of deep learning approaches that prospects the detection and recognition of text, effectively...
For the disadvantage of high cost and poor practicability of traditional license plate recognition technology based on PC, apply this technology on the ZYNQ to implement the hardware acceleration of the license plate recognition algorithm. The platform consists of programmable logic (PL)and a processing system (PS). The hardware acceleration of the algorithm of license plate location is completed...
Feature extraction is the process of mapping input signal to informative representation that can easily be handled by the classifier systems to build decision boundary in between the participating pattern classes. Scattering representation build invariant signal representation by applying a cascade of wavelet decompositions and complex modulus, followed by low-pass filtering. The objective of this...
License plate recognition (LPR) system is an important system in our life. LPR is an image processing and a character recognition system that used to recognize any car from the others. An automatic license plate recognition system for the three different Iraqi car license plates was proposed in this paper. Differentiating between the three styles were done depending on the plate size. An optical character...
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