.png){kind=icon}
.png){kind=icon}
Return
Deep OCR for Arabic script-based language like Pastho
.png){kind=icon}
PRE
AI
DOI：10.1111/exsy.12565.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Developing cursive script recognition systems have always been a challenging task for researchers. This article proposes a ligature-based recognition system for the cursive Pashto script using four pre-trained CNN models using a fine-tuned approach. The SqueezeNet, ResNet, MobileNet and DenseNet models have been observed for the classification and the recognition of Pashto sub-word (ligature). Overall, the proposed system is divided into two domains (Source and Target). The source domain contains the pre-trained models used on the ImageNet Dataset. These models are later fine-tuned using the transfer learning approach to be used for the Pashto ligature recognition. The data augmentation techniques of negative and contour are used to increase the representation of ligature images and the dataset size. The CNN models have been evaluated on the benchmarks Pashto ligatures FAST-NU dataset. The proposed system achieved the highest recognition rate of up to 99.31% using the DenseNet architecture of Convolutional Neural Network for Pashto ligature.
Keywords:
DenseNet
ligature recognition
MobileNet
ResNet
SqueezeNet
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.