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Handwritten character recognition is an essential part of optical character recognition domain. Bangla handwritten compound character recognition is a complex task that is challenging due to extensive size of and sheer diversity within the alphabet. The current work proposes a novel method of recognition of compound characters in Bangla language using deep convolutional neural networks (DCNN) and...
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...
Many applications require both the location and identity of objects in images and video. Most existing solutions, like QR codes, AprilTags, and ARTags use complex machine-readable fiducial markers with heuristically derived methods for detection and classification. However, in applications where humans are integral to the system and need to be capable of locating objects in the environment, fiducial...
A practical method to improve the performance of off-line Handwritten Chinese Character Recognition (HCCR) was proposed in this paper. The center loss was used in face verification task to optimize intra-class distance. With the joint supervision of softmax loss and center loss, A light convolutional neural networks (CNNs) framework was trained for off-line HCCR which could optimize inter and intra...
Optical Character Recognition is the process of converting an input text image into a machine encoded format. Different methods are used in OCR for different languages. The main steps of optical character recognition are pre-processing, segmentation and recognition. Recognizing handwritten text is harder than recognizing printed text. Convolutional Neural Network has shown remarkable improvement in...
This paper proposes a novel segmentation-free approach using deep neural network based hidden Markov model (DNN-HMM) for offline handwritten Chinese text recognition. In the general Bayesian framework, three key issues are comprehensively investigated, namely feature extraction, character modeling, and language modeling. First, as for the feature extraction on the basis of each frame or sliding window,...
In recent years, growing attention has been paid to recognizing text in natural scenes images. Scene Character recognition (SCR) is an important step in automatizing the process of reading text in natural scenes.
Recognizing text in natural images can be a useful tool for image understanding. We focus on the detection problem, which is to find regions in an image occupied by text. We consider multi-layered convolutional neural networks as a means to classify local regions as text or not, and take a sliding-window approach to scan a full image. For training we generate large synthetic datasets to complement...
News video caption, which carries main contents of related news story, plays an important role in content-based video analysis and retrieval system. In this paper, the convolutional neural network (CNN) is used to the recognition of chinese caption text in news video. First, the color and edge feature are used for caption location. Then, the segmentation combined Otsu and K-means clustering algorithm...
Convolutional Neural Networks (CNN) are on the forefront of accurate character recognition. This paper explores CNNs at their maximum capacity by implementing the use of large datasets. We show a near-perfect performance by using a dataset of about 820,000 real samples of isolated handwritten digits, much larger than the conventional MNIST database. In addition, we report a near-perfect performance...
Paper describes an investigation of simplified neocognitron neural network model as a tool for practical recognition of handwritten mark images. Simplification of neocognitron structure from only two stages and fixed number of feature-extraction planes is proposed, the overall stages of solving practical image processing problem are described. Recognition properties of simplified net are investigated,...
Character recognition technique associates a symbolic identity with the image of a character. Different characters and languages have different structures and features. Lampung character and language are different with any other languages. We have developed Lampung handwritten character recognition using back-propagation neural networks. However since some Lampung characters have similar features,...
Handwritten character recognition has been one of the most fascinating research among the various researches in field of image processing. In Handwritten character recognition method the input is scanned from images, documents and real time devices like tablets, tabloids, digitizers etc. which are then interpreted into digital text. There are basically two approaches — Online Handwritten recognition...
Optical character recognition has been extensively investigated in the past few years. Many existing techniques are able to provide high recognition rate, but at the cost of long training time. In this work, we present a neural network based approach to reduce the training time while maintain the high recognition rate. The main idea is to perform a preprocessing stage to partition the training data...
A recognition algorithm for similar characters on license plates based on convolution neural networks (CNN) is proposed in this study to improve the recognition rate in a complex environment. The algorithm adopts the improved CNN to recognize similar letters and numbers in license plate images. Experimental results suggest that the improved CNN may improve the recognition rate and speed of similar...
In this paper, we introduce a new public image dataset for Devanagari script: Devanagari Handwritten Character Dataset (DHCD). Our dataset consists of 92 thousand images of 46 different classes of characters of Devanagari script segmented from handwritten documents. We also explore the challenges in recognition of Devanagari characters. Along with the dataset, we also propose a deep learning architecture...
This paper proposes a novel algorithm to automatically solve a Sudoku puzzle on images taken from magazines or computer game software. The process involves preprocessing the image, extracting characters, recognizing characters and solving puzzle, which all are specifically designed for Sudoku. The algorithm effectiveness is verified with images taken from cameras. In this paper, a pixel to pixel comparison...
Currently, the optical character recognition (OCR) is applied in many fields such as reading the office letter and to read the serial on parts of industrial. The most manufacturing focus the processing time and accuracy for inspection process. The learning method of the optical character recognition is used a neural network to recognize the fonts and correlation the matching value. The neural network...
This paper presents a novel irrelevant variability normalization (IVN) approach via hierarchical deep neural networks (HDNNs) and prototype-based classifier for online handwritten Chinese character recognition. The recent insight of deep neural network (DNN) is the deep architecture with large training data can bring the best performance in many research areas. The architecture design of our proposed...
This paper presents a Tibetan component representation learning method for component-based online handwritten Tibetan character recognition. In conventional methods, we designed features manually for Tibetan components. The hand-crafted features are often incomplete and decrease the component recognition accuracy, which influences component-based character recognition performance. To overcome the...
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