Deep Learning Techniques for Optical Character Recognition

2021 ◽  
pp. 339-349
Author(s):  
Naragudem Sarika ◽  
NageswaraRao Sirisala
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Optical Character ◽  
Learning Techniques

scholarly journals Research on Deep Learning Techniques in Breaking Text-Based Captchas and Designing Image-Based Captcha

2021 ◽  
pp. 266-269
Author(s):  
Janarthanan A ◽  
Pandiyarajan C ◽  
Sabarinathan M ◽  
Sudhan M ◽  
Kala R
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Experimental Results ◽  
Text Recognition ◽  
Image Resizing ◽  
Optical Character ◽  
Learning Techniques ◽  
Text Images

Optical character recognition (OCR) is a process of text recognition in images (one word). The input images are taken from the dataset. The collected text images are implemented to pre-processing. In pre-processing, we can implement the image resize process. Image resizing is necessary when you need to increase or decrease the total number of pixels, whereas remapping can occur when you are zooming refers to increase the quantity of pixels, so that when you zoom an image, you will see clear content. After that, we can implement the segmentation process. In segmentation, we can segment the each characters in one word. We can extract the features values from the image that means test feature. In classification process, we have to classify the text from the image. Image classification is performed the images in order to identify which image contains text. A classifier is used to identify the image containing text. The experimental results shows that the accuracy.

Optical Character Recognition Using Deep Learning Techniques for Printed and Handwritten Documents

2020 ◽  
Author(s):  
Sanika Bagwe ◽  
Vruddhi Shah ◽  
Jugal Chauhan ◽  
Purvi Harniya ◽  
Amanshu Tiwari ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Handwritten Documents ◽  
Optical Character ◽  
Learning Techniques

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.

scholarly journals Pashtu Numerals Recognition through Convolutional Neural Networks

2019 ◽  
pp. 91-96
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Accuracy ◽  
Research Use ◽  
Optical Character ◽  
Classification Tasks ◽  
Scanned Images

In the proposed paper we introduce a new Pashtu numerals dataset having handwritten scanned images. We make the dataset publically available for scientific and research use. Pashtu language is used by more than fifty million people both for oral and written communication, but still no efforts are devoted to the Optical Character Recognition (OCR) system for Pashtu language. We introduce a new method for handwritten numerals recognition of Pashtu language through the deep learning based models. We use convolutional neural networks (CNNs) both for features extraction and classification tasks. We assess the performance of the proposed CNNs based model and obtained recognition accuracy of 91.45%.

scholarly journals YORÙBÁNET: A DEEP CONVOLUTIONAL NEURAL NETWORK DESIGN FOR YORÙBÁ ALPHABETS RECOGNITION

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Oyeniran Oluwashina Akinloye ◽  
Oyebode Ebenezer Olukunle
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Design ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Test Set ◽  
Novel Technique ◽  
Optical Character

Numerous works have been proposed and implemented in computerization of various human languages, nevertheless, miniscule effort have also been made so as to put Yorùbá Handwritten Character on the map of Optical Character Recognition. This study presents a novel technique in the development of Yorùbá alphabets recognition system through the use of deep learning. The developed model was implemented on Matlab R2018a environment using the developed framework where 10,500 samples of dataset were for training and 2100 samples were used for testing. The training of the developed model was conducted using 30 Epoch, at 164 iteration per epoch while the total iteration is 4920 iterations. Also, the training period was estimated to 11296 minutes 41 seconds. The model yielded the network accuracy of 100% while the accuracy of the test set is 97.97%, with F1 score of 0.9800, Precision of 0.9803 and Recall value of 0.9797.

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