Neural Computing for Online Arabic Handwriting Character Recognition using Hard Stroke Features Mining

• URL: http://arxiv.org/abs/2005.02171v3
• Date: Fri, 15 Jan 2021 10:58:36 GMT
• Title: Neural Computing for Online Arabic Handwriting Character Recognition using Hard Stroke Features Mining
• Authors: Amjad Rehman (PSU and UTM)
• Abstract summary: An enhanced method of detecting the desired critical points from vertical and horizontal direction-length of handwriting stroke features of online Arabic script recognition is proposed. A minimum feature set is extracted from these tokens for classification of characters using a multilayer perceptron with a back-propagation learning algorithm and modified sigmoid function-based activation function. The proposed method achieves an average accuracy of 98.6% comparable in state of art character recognition techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online Arabic cursive character recognition is still a big challenge due to the existing complexities including Arabic cursive script styles, writing speed, writer mood and so forth. Due to these unavoidable constraints, the accuracy of online Arabic character's recognition is still low and retain space for improvement. In this research, an enhanced method of detecting the desired critical points from vertical and horizontal direction-length of handwriting stroke features of online Arabic script recognition is proposed. Each extracted stroke feature divides every isolated character into some meaningful pattern known as tokens. A minimum feature set is extracted from these tokens for classification of characters using a multilayer perceptron with a back-propagation learning algorithm and modified sigmoid function-based activation function. In this work, two milestones are achieved; firstly, attain a fixed number of tokens, secondly, minimize the number of the most repetitive tokens. For experiments, handwritten Arabic characters are selected from the OHASD benchmark dataset to test and evaluate the proposed method. The proposed method achieves an average accuracy of 98.6% comparable in state of art character recognition techniques.

Related papers

• HierCode: A Lightweight Hierarchical Codebook for Zero-shot Chinese Text Recognition [47.86479271322264]
  We propose HierCode, a novel and lightweight codebook that exploits the innate hierarchical nature of Chinese characters. HierCode employs a multi-hot encoding strategy, leveraging hierarchical binary tree encoding and prototype learning to create distinctive, informative representations for each character. This approach not only facilitates zero-shot recognition of OOV characters by utilizing shared radicals and structures but also excels in line-level recognition tasks by computing similarity with visual features.
  arXiv  Detail & Related papers  (2024-03-20T17:20:48Z)
• Chinese Text Recognition with A Pre-Trained CLIP-Like Model Through Image-IDS Aligning [61.34060587461462]
  We propose a two-stage framework for Chinese Text Recognition (CTR) We pre-train a CLIP-like model through aligning printed character images and Ideographic Description Sequences (IDS) This pre-training stage simulates humans recognizing Chinese characters and obtains the canonical representation of each character. The learned representations are employed to supervise the CTR model, such that traditional single-character recognition can be improved to text-line recognition.
  arXiv  Detail & Related papers  (2023-09-03T05:33:16Z)
• Huruf: An Application for Arabic Handwritten Character Recognition Using Deep Learning [0.0]
  We propose a lightweight Convolutional Neural Network-based architecture for recognizing Arabic characters and digits. The proposed pipeline consists of a total of 18 layers containing four layers each for convolution, pooling, batch normalization, dropout, and finally one Global average layer. The proposed model respectively achieved an accuracy of 96.93% and 99.35% which is comparable to the state-of-the-art and makes it a suitable solution for real-life end-level applications.
  arXiv  Detail & Related papers  (2022-12-16T17:39:32Z)
• Kurdish Handwritten Character Recognition using Deep Learning Techniques [26.23274417985375]
  This paper attempts to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. A comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The tested results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate.
  arXiv  Detail & Related papers  (2022-10-18T16:48:28Z)
• Letter-level Online Writer Identification [86.13203975836556]
  We focus on a novel problem, letter-level online writer-id, which requires only a few trajectories of written letters as identification cues. A main challenge is that a person often writes a letter in different styles from time to time. We refer to this problem as the variance of online writing styles (Var-O-Styles)
  arXiv  Detail & Related papers  (2021-12-06T07:21:53Z)
• Skeleton Based Sign Language Recognition Using Whole-body Keypoints [71.97020373520922]
  Sign language is used by deaf or speech impaired people to communicate. Skeleton-based recognition is becoming popular that it can be further ensembled with RGB-D based method to achieve state-of-the-art performance. Inspired by the recent development of whole-body pose estimation citejin 2020whole, we propose recognizing sign language based on the whole-body key points and features.
  arXiv  Detail & Related papers  (2021-03-16T03:38:17Z)
• Arabic Handwritten Character Recognition based on Convolution Neural Networks and Support Vector Machine [0.0]
  We present an algorithm for recognizing Arabic letters and characters based on using deep convolution neural networks (DCNN) and support vector machine (SVM) This paper addresses the problem of recognizing the Arabic handwritten characters by determining the similarity between the input templates and the pre-stored templates. The experimental results of this work indicate the ability of the proposed algorithm to recognize, identify, and verify the input handwritten Arabic characters.
  arXiv  Detail & Related papers  (2020-09-28T16:18:52Z)
• An Efficient Language-Independent Multi-Font OCR for Arabic Script [0.0]
  This paper proposes a complete Arabic OCR system that takes a scanned image of Arabic Naskh script as an input and generates a corresponding digital document. This paper also proposes an improved font-independent character algorithm that outperforms the state-of-the-art segmentation algorithms.
  arXiv  Detail & Related papers  (2020-09-18T22:57:03Z)
• A Skip-connected Multi-column Network for Isolated Handwritten Bangla Character and Digit recognition [12.551285203114723]
  We have proposed a non-explicit feature extraction method using a multi-scale multi-column skip convolutional neural network. Our method is evaluated on four publicly available datasets of isolated handwritten Bangla characters and digits.
  arXiv  Detail & Related papers  (2020-04-27T13:18:58Z)
• Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild [103.51604161298512]
  We propose an adversarial learning framework for the generation and recognition of multiple characters in an image. Our framework can be integrated into recent recognition methods to achieve new state-of-the-art recognition accuracy.
  arXiv  Detail & Related papers  (2020-01-13T12:41:42Z)
• TextScanner: Reading Characters in Order for Robust Scene Text Recognition [60.04267660533966]
  TextScanner is an alternative approach for scene text recognition. It generates pixel-wise, multi-channel segmentation maps for character class, position and order. It also adopts RNN for context modeling and performs paralleled prediction for character position and class.
  arXiv  Detail & Related papers  (2019-12-28T07:52:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.