An Automated Pipeline for Robust Image Processing and Optical Character Recognition of Historical Documents

2020 ◽  
pp. 166-175
Author(s):  
Ivan Gruber ◽  
Pavel Ircing ◽  
Petr Neduchal ◽  
Marek Hrúz ◽  
Miroslav Hlaváč ◽  
...  
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Historical Documents ◽  
Optical Character ◽  
Automated Pipeline ◽  
Robust Image

An Useful Review on Optical Character Recognition for Smart Era Generation

2021 ◽  
pp. 1-41
Author(s):  
Abhishek Das ◽  
Mihir Narayan Mohanty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Deep Neural Network ◽  
Machine Learning Method ◽  
Learning Method ◽  
Result Section ◽  
Optical Character

In this chapter, the authors have reviewed on optical character recognition. The study belongs to both typed characters and handwritten character recognition. Online and offline character recognition are two modes of data acquisition in the field of OCR and are also studied. As deep learning is the emerging machine learning method in the field of image processing, the authors have described the method and its application of earlier works. From the study of the recurrent neural network (RNN), a special class of deep neural network is proposed for the recognition purpose. Further, convolutional neural network (CNN) is combined with RNN to check its performance. For this piece of work, Odia numerals and characters are taken as input and well recognized. The efficacy of the proposed method is explained in the result section.

scholarly journals Citizen Id Card Detection using Image Processing and Optical Character Recognition

2019 ◽  
Vol 1235 ◽  
pp. 012049
Author(s):  
Wira Satyawan ◽  
M Octaviano Pratama ◽  
Rini Jannati ◽  
Gibran Muhammad ◽  
Bagus Fajar ◽  
...  
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Optical Character

Application of Mathematical Morphology on Touching or Broken Characters Processing

2010 ◽  
Vol 171-172 ◽  
pp. 73-77
Author(s):  
Ying Jie Liu ◽  
Fu Cheng You
Keyword(s):  
Image Processing ◽  
Mathematical Morphology ◽  
Digital Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Binary Image ◽  
Practical Applications ◽  
Optical Character ◽  
Individual Character

It is difficult to process touching or broken characters in practical applications on optical character recognition. For touching or broken characters, a method based on mathematical morphology of binary image is put forward in the paper. On the basis of the relative theories of digital image processing, the overall process is introduced including separation of touching characters and connection of broken characters. First of all, character image is pre-processed through smoothing and threshold segmentation in order to generate binary image of characters. Then character regions which are touching or broken are processed through different operators of mathematical morphology of binary image by different structuring elements. Thus the touching characters are separated and broken characters are connected. For higher recognition rate, further processes are done to achieve normal and individual character regions.

scholarly journals iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing

2021 ◽  
Author(s):  
Menbere Kina Tekleyohannes ◽  
Vladimir Rybalkin ◽  
Muhammad Mohsin Ghaffar ◽  
Javier Alejandro Varela ◽  
Norbert Wehn ◽  
...  
Keyword(s):  
Power Consumption ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
High Accuracy ◽  
Document Image ◽  
System On Chip ◽  
Historical Documents ◽  
Optical Character ◽  
Historical Archives ◽  
On Chip

AbstractIn recent years, $$\hbox {optical character recognition (OCR)}$$ optical character recognition (OCR) systems have been used to digitally preserve historical archives. To transcribe historical archives into a machine-readable form, first, the documents are scanned, then an $$\hbox {OCR}$$ OCR is applied. In order to digitize documents without the need to remove them from where they are archived, it is valuable to have a portable device that combines scanning and $$\hbox {OCR}$$ OCR capabilities. Nowadays, there exist many commercial and open-source document digitization techniques, which are optimized for contemporary documents. However, they fail to give sufficient text recognition accuracy for transcribing historical documents due to the severe quality degradation of such documents. On the contrary, the anyOCR system, which is designed to mainly digitize historical documents, provides high accuracy. However, this comes at a cost of high computational complexity resulting in long runtime and high power consumption. To tackle these challenges, we propose a low power energy-efficient accelerator with real-time capabilities called iDocChip, which is a configurable hybrid hardware-software programmable $$\hbox {System-on-Chip (SoC)}$$ System-on-Chip (SoC) based on anyOCR for digitizing historical documents. In this paper, we focus on one of the most crucial processing steps in the anyOCR system: Text and Image Segmentation, which makes use of a multi-resolution morphology-based algorithm. Moreover, an optimized $$\hbox {FPGA}$$ FPGA -based hybrid architecture of this anyOCR step along with its optimized software implementations are presented. We demonstrate our results on multiple embedded and general-purpose platforms with respect to runtime and power consumption. The resulting hardware accelerator outperforms the existing anyOCR by 6.2$$\times$$ × , while achieving 207$$\times$$ × higher energy-efficiency and maintaining its high accuracy.

scholarly journals Custom OCR for Identity Documents:OCRXNet

2020 ◽  
Vol 2 (2) ◽  
pp. 112-119
Author(s):  
Kawal Arora ◽  
Ankur Singh Bist ◽  
Roshan Prakash ◽  
Saksham Chaurasia
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Real World ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Good Accuracy ◽  
Processing Methods ◽  
Optical Character ◽  
Learning Techniques ◽  
Real World Applications

Recent advancements in the area of Optical Character Recognition (OCR) using deep learning techniques made it possible to use for real world applications with good accuracy. In this paper we present a system named as OCRXNet. OCRXNetv1, OCRXNetv2 and OCRXNetv3 are proposed and compared on different identity documents. Image processing methods and various text detectors have been used to identify best fitted process for custom ocr of identity documents. We also introduced the end to end pipeline to implement OCR for various use cases.

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