Handwritten Compound Characters Research Articles

A squeeze and excitation ResNeXt-based deep learning model for Bangla handwritten compound character recognition

In today’s digital era, the recognition of handwritten documents is highly demanding due to the widespread applications. Bangla is one of the most used languages in the world, which consists of 50 basic alphabets. Many compound characters exist in Bangla, which is the combination of two or more basic characters. The recognition of Bangla handwritten characters is a challenging task due to their various sizes, sheer, diversity, lots of turns, similarities between alphabets, and different writing patterns. This paper presents a deep CNN (Convolutional Neural Network) model using the SE-ResNeXt. The squeeze and excitation (SE) blocks are added with existing ResNeXt to address the Bangla handwritten compound character recognition. Usually, CNN extracted the spatial features in the lower-layers and complex features in the upper layers. The SE blocks are added to improve the performance of usual deep CNN by automatically fusing the channel-wise spatial information and inter-channel dependencies through squeezing and excitation within local receptive fields, respectively. To validate the performance of the proposed model, we have utilized Mendeley BanglaLekha-Isolated 2 dataset. The experimental results demonstrate that the proposed model exhibits an average accuracy of 99.82% in recognizing Bangla handwritten compound characters. In addition, the proposed model outperforms the state-of-the-art models by demonstrating higher results.

Template Matching for Marathi Handwritten Compound Character Recognition

Template Matching for Marathi Handwritten Compound Character Recognition

Shape decomposition-based handwritten compound character recognition for Bangla OCR

Proper recognition of complex-shaped handwritten compound characters is still a big challenge for Bangla OCR systems. In this paper, we propose a novel shape decomposition-based segmentation technique to decompose the compound characters into prominent shape components. This shape decomposition reduces the classification complexity in terms of less number of classes to recognize, and at the same time improves the recognition accuracy. The decomposition is done at the segmentation area where the two basic shapes are joined to form a compound character. We use chain code histogram feature set with multi-layer perceptron (MLP) based classifier with backpropagation learning for classification. On experimentation, the proposed method is observed to provide good recognition accuracy comparing with other existing methods.

A benchmark image database of isolated Bangla handwritten compound characters

In the present work, we present a benchmark image database of isolated handwritten Bangla compound characters, used in the standard Bangla literature. A thorough survey over more than 2 million Ban...