Deep Convolutional Neural Networks for Recognition of Historical Handwritten Kannada Characters

Chandrakala, H T; Thippeswamy, G.

doi:10.1007/978-981-13-9920-6_7

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…Chandrakala et.al in [12], considers 11th century digitized kannada epigraphical script for recognition using deep neural networks. Features Pinkesh et.al [14] proposed a bidirectional long short term memory model to segment sentence embedding using CNN model.…”

Section: Segmentation Using Machine Learning Modelsmentioning

confidence: 99%

Region-Based Convolutional Neural Network for Segmenting Text in Epigraphical Images

Preethi

Mamatha²

2022

AIA

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Indian history derived from ancient writings on the inscriptions, palm leaves, copper plates, coins, and many more mediums. Epigraphers read these inscriptions and produce meaningful interpretations. Automating the process of reading is the interest of our study and in this paper, segmentation to detect text on digitized inscriptional images is dealt in detail. Character segmentation from Epigraphical images helps in optical character recognizer (OCR) in training and recognition of old regional scripts. Epigraphical images are drawn from estampages containing scripts from various periods starting from Brahmi in the 3rd century BC to the medieval period of the 15th century AD. The scripts or characters present in digitized epigraphical images are illegible and have complex noisy background textures. To achieve script/text segmentation, region based convolutional neural network (CNN) is employed to detect characters in the images. Proposed method uses selective search to identify text regions and forwards them to trained CNN models for drawing feature vectors. These feature vectors are fed to support vector machine (SVM) classifiers for classification and recognize text by drawing a bounding box based on confidence score. Alexnet, VGG16, Resnet50 and InceptionV3 are used as CNN models for experimentation and InceptionV3 performed well with good results. 197 images are used for experimentation, out of which 70 samples are of printed denoised epigraphical images, 40 denoised estampage images and 87 noisy estampage images. The segmentation result of 74.79% for printed denoised epigraphical images,71.53 % for denoised estampage epigraphical images and 18.11% for noisy estampage images are recorded by InceptionV3. The segmented characters are used for epigraphical applications like period/era prediction and Recognition of characters. FAST and FASTER Region based design approach was also tested and illustrated in this paper.

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Section: Segmentation Using Machine Learning Modelsmentioning

confidence: 99%

Region-Based Convolutional Neural Network for Segmenting Text in Epigraphical Images

Preethi

Mamatha²

2022

AIA

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“…An accuracy of 97.36% achieved with Kannada-MNIST and 79.06% with Dig-MNIST respectively. A deep convolution network is deployed by Chandrakala and Thippeswamy [23] to recognize historical Kannada handwritten characters. Feature extraction and classification is unified by the model.…”

Section: Introductionmentioning

confidence: 99%

Recognition of compound characters in Kannada language

Narasimhaiah

Rangarajan

2022

IJECE

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<p><span lang="EN-US">Recognition of degraded printed compound Kannada characters is a challenging research problem. It has been verified experimentally that noise removal is an essential preprocessing step. Proposed are two methods for degraded Kannada character recognition problem. Method 1 is conventionally used histogram of oriented gradients (HOG) feature extraction for character recognition problem. Extracted features are transformed and reduced using principal component analysis (PCA) and classification performed. Various classifiers are experimented with. Simple compound character classification is satisfactory (more than 98% accuracy) with this method. However, the method does not perform well on other two compound types. Method 2 is deep convolutional neural networks (CNN) model for classification. This outperforms HOG features and classification. The highest classification accuracy is found as 98.8% for simple compound character classification. The performance of deep CNN is far better for other two compound types. Deep CNN turns out to better for pooled character classes.</span></p>

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“…Observed accuracies as follows: Kannada-MNISTwith 97.36%,Dig-MNIST with 79.06%,augmented maintest set and augmented Dig-MNIST with 96.32% and 77.31% respectively. Chandrakala H. T and Thippeswamy G [10] deployed Deep convolution neural network for the recognition of historical Kannada handwritten characters. Experiments carried out on digitized E-stampages using SVM classifier and an accuracy of 70% is achieved.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Degraded Printed Kannada Character Recognition

T.N¹,

LalithaRangarajan²

2021

INDJCSE

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Recognition of degraded printed Kannada characters is a challenging research problem. Proposed in this paper is a deep convolutional neural network for recognition of degraded printed Kannada characters. Characters in some old Kannada texts are affected by various degradations that result in breakages and dilations of characters introducing challenges in the process of recognition. The architecture consists of three levels, the first two levels with ReLu, Max pooling layers, and the third level with just ReLu. The output of these is input to fully connected layer which performs classification of characters. Experimental analysis is carried out using 156 classes of characters each class with 100 instances. Performance is evaluated for 4 epochs with 60 iterations per epoch. Highest classification accuracy of 99.51% has been reported for 75% training.

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Deep Convolutional Neural Networks for Recognition of Historical Handwritten Kannada Characters

Cited by 8 publications

References 10 publications

Region-Based Convolutional Neural Network for Segmenting Text in Epigraphical Images

Region-Based Convolutional Neural Network for Segmenting Text in Epigraphical Images

Recognition of compound characters in Kannada language

Deep Convolutional Neural Networks for Degraded Printed Kannada Character Recognition

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