Global Binarization of Document Images Using a Neural Network

2007 ◽  
Author(s):  
Adnan Khashman ◽  
Boran Sekeroglu
Keyword(s):  
Neural Network ◽  
Document Images

scholarly journals Design of an OCR System and its Hardware Implementation

2021 ◽  
Vol 9 (12) ◽  
pp. 159-174
Author(s):  
Gulfeshan Parween
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Character Recognition ◽  
Hardware Implementation ◽  
Gray Level ◽  
Document Images ◽  
English Alphabet ◽  
Barcode Recognition ◽  
Printed Text ◽  
Artificial Neural

Abstract: In this paper, we present a scheme to develop to complete OCR system for printed text English Alphabet of Uppercase of different font and of different sizes so that we can use this system in Banking, Corporate, Legal industry and so on. OCR system consists of different modules like preprocessing, segmentation, feature extraction and recognition. In preprocessing step it is expected to include image gray level conversion, binary conversion etc. After finding out the feature of the segmented characters artificial neural network and can be used for Character Recognition purpose. Efforts have been made to improve the performance of character recognition using artificial neural network techniques. The proposed OCR system is capable of accepting printed document images from a file and implemented using MATLAB R2014a version. Key words: OCR, Printed text, Barcode recognition

Key Character Detection Based on Deep Neural Network for Document Images

2018 ◽  
Author(s):  
Jia-Cheng Tu ◽  
Guo-Shiang Lin ◽  
Chao-Chuan Chang ◽  
Kuan-Cheng Huang ◽  
Ming-Hsien Tasi ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Document Images

scholarly journals MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction

2021 ◽  
Author(s):  
Guozhi Tang ◽  
Lele Xie ◽  
Lianwen Jin ◽  
Jiapeng Wang ◽  
Jingdong Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Information Extraction ◽  
Visual Information ◽  
Classification Problem ◽  
Document Images ◽  
Matching Model ◽  
Semantic Categories ◽  
Multimodal Features ◽  
Effective Operation ◽  
Font Color

Visual Information Extraction (VIE) task aims to extract key information from multifarious document images (e.g., invoices and purchase receipts). Most previous methods treat the VIE task simply as a sequence labeling problem or classification problem, which requires models to carefully identify each kind of semantics by introducing multimodal features, such as font, color, layout. But simply introducing multimodal features can't work well when faced with numeric semantic categories or some ambiguous texts. To address this issue, in this paper we propose a novel key-value matching model based on a graph neural network for VIE (MatchVIE). Through key-value matching based on relevancy evaluation, the proposed MatchVIE can bypass the recognitions to various semantics, and simply focuses on the strong relevancy between entities. Besides, we introduce a simple but effective operation, Num2Vec, to tackle the instability of encoded values, which helps model converge more smoothly. Comprehensive experiments demonstrate that the proposed MatchVIE can significantly outperform previous methods. Notably, to the best of our knowledge, MatchVIE may be the first attempt to tackle the VIE task by modeling the relevancy between keys and values and it is a good complement to the existing methods.

Behaviour-Based Clustering of Neural Networks

2011 ◽  
pp. 231-235
Author(s):  
María José Castro-Bleda ◽  
Slavador España-Boquera ◽  
Francisco Zamora-Martínez
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Clustering Algorithm ◽  
Training Data ◽  
Document Images ◽  
Supervised Classifiers ◽  
Overall Performance ◽  
Printed Text

The field of off-line optical character recognition (OCR) has been a topic of intensive research for many years (Bozinovic, 1989; Bunke, 2003; Plamondon, 2000; Toselli, 2004). One of the first steps in the classical architecture of a text recognizer is preprocessing, where noise reduction and normalization take place. Many systems do not require a binarization step, so the images are maintained in gray-level quality. Document enhancement not only influences the overall performance of OCR systems, but it can also significantly improve document readability for human readers. In many cases, the noise of document images is heterogeneous, and a technique fitted for one type of noise may not be valid for the overall set of documents. One possible solution to this problem is to use several filters or techniques and to provide a classifier to select the appropriate one. Neural networks have been used for document enhancement (see (Egmont-Petersen, 2002) for a review of image processing with neural networks). One advantage of neural network filters for image enhancement and denoising is that a different neural filter can be automatically trained for each type of noise. This work proposes the clustering of neural network filters to avoid having to label training data and to reduce the number of filters needed by the enhancement system. An agglomerative hierarchical clustering algorithm of supervised classifiers is proposed to do this. The technique has been applied to filter out the background noise from an office (coffee stains and footprints on documents, folded sheets with degraded printed text, etc.).

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