Handwriting segmentation of unconstrained Oriya text

Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper we propose a water reservoir concept-based scheme for segmentation of unconstrained Oriya handwritten text into individual characters. Here, at first, the text image is segmented into lines, and the lines are then segmented into individual words. For line segmentation, the document is divided into vertical stripes. Analysing the heights of the water reservoirs obtained from different components of the document, the width of a stripe is calculated. Stripe-wise horizontal histograms are then computed and the relationship of the peak-valley points of the histograms is used for line segmentation. Based on vertical projection profiles and structural features of Oriya characters, text lines are segmented into words. For character segmentation, at first, the isolated and connected (touching) characters in a word are detected. Using structural, topological and water reservoir concept-based features, characters of the word that touch are then segmented. From experiments we have observed that the proposed “touching character” segmentation module has 96.7% accuracy for two-character touching strings.     

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The projected SFODTL-AHCR model primarily focuses on identifying the handwritten Arabic characters in the input image. The proposed SFODTL-AHCR model pre-processes the input image by following the Histogram Equalization approach to attain this objective. The Inception with ResNet-v2 model examines the pre-processed image to produce the feature vectors. The Deep Wavelet Neural Network (DWNN) model is utilized to recognize the handwritten Arabic characters. At last, the SFO algorithm is utilized for fine-tuning the parameters involved in the DWNN model to attain better performance. The performance of the proposed SFODTL-AHCR model was validated using a series of images. Extensive comparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches.     

An Efficient Hybrid Model for Arabic Text Recognition

In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term Memory (BLSTM) followed by a Connectionist Temporal Classification layer (CTC). Moreover, during the training phase of the model, we introduce an algorithm of data augmentation to increase the quality of data. Our proposed approach can recognize Arabic handwritten texts without the need to segment the characters, thus overcoming several problems related to this point. To train and test (evaluate) our approach, we used two Arabic handwritten text recognition databases, which are IFN/ENIT and KHATT. The Experimental results show that our new approach, compared to other methods in the literature, gives better results.     

EMS表单中手写体中文识别图像预处理方法研究

目的在脱机手写体文字识别系统中,由于自由书写的字符不可避免地受到图像背景不均匀、图像倾斜和字符粘连及大小不一等因素的影响,为了确保字符切分和识别的正确性,对EMS表单中手写体汉字字符图像预处理方法进行探讨,展示了EMS表单图像预处理的全过程。方法采用最小二乘法作拟合直线的方法,对目标图像进行定位和分割,用基于大津阈值的分块阈值算法处理目标图像的背景不均问题,并减少噪声干扰。结果该图像预处理方法在1020张真实EMS图像上进行测试,识别正确率达到了86.3%。结论该方法有一定的灵活性和抗干扰性,减少了图像噪声对汉字字符切分和识别的影响。     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.     

Recognition of online unconstrained handwritten Gurmukhi characters based on Finite State Automata

This paper presents a language-based efficient post-processing algorithm for the recognition of online unconstrained handwritten Gurmukhi characters. A total of 93 stroke classes have been identified to recognize the Gurmukhi character set in this work. Support Vector Machine (SVM) classifier has been employed for stroke classification. The main objective of this paper is to improve the character level recognition accuracy using an efficient Finite State Automata (FSA)-based formation of Gurmukhi characters algorithm. A database of 21,945 online handwritten Gurmukhi words is primarily collected in this experiment. After analysing the collected database, we have observed that a character can be written using one or more strokes. Therefore, a total of 65,946 strokes have been annotated using the 93 identified stroke classes. Among these strokes, 15,069 stroke samples are considered for training the classifier. The proposed system achieved promising recognition accuracy of 97.3% for Gurmukhi characters, when tested with a new database of 8,200 characters, written by 20 different writers.     

H-Cb混合颜色模型下快递单手写体提取算法研究

目的在不受光照条件的影响下,利用H-Cb混合颜色模型,提取快递单底单图像手写体文字信息。方法首先将图像从RGB颜色空间分别转换到HSI颜色空间和YCbCr颜色空间;然后将改进的YCbCr颜色空间的Cb颜色分量与HSI颜色空间的H颜色分量进行信息融合;最后对提取出的手写体文字信息进行阈值和反相处理,并将该算法提取结果与基于YCbCr颜色空间Cb颜色分量阈值分割方法和基于Lab颜色空间的手写文字聚类算法的提取结果,在分割效果、文字识别率上进行对比。结果利用H-Cb混合颜色模型检测出的手写体文字更准确,具有更高的识别率,在理想文字切分条件下识别率达96%。结论使用H-Cb混合颜色模型提取手写文字受光照条件影响小,提取出的图像噪声小、识别率高,算法简单可行,为彩色图像的检测与判定技术提供了支撑。     

基于 YCbCr 颜色空间的快递单手写文字分割

目的在YCbCr颜色空间下,利用Cb颜色分量信息结合阈值分割方法,提取快递单图像手写体文字信息。方法首先将图像从RGB颜色空间转换到YCbCr颜色空间下,然后在Cb颜色分量图像下进行图像阈值分割处理操作,最后对提取出的手写体文字信息进行中值滤波去噪处理,并将该算法提取的结果与基于YCbCr颜色空间使用K均值聚类方法提取的结果在分割效果、分割时间与文字识别率上进行对比。结果利用Cb颜色分量提取出的手写体文字信息更清晰,具有更快的处理速度和更高的识别率,快递单图像平均处理时间为1.36 s,识别率为89%。结论单独利用Cb颜色分量信息提取手写文字就可得到较好的提取效果,算法简单、可行。     

基于 Lab 颜色空间的手写文字提取算法研究

目的研究颜色空间聚类在彩色手写体文字提取方面的应用。方法分别在Lab,LUV,YCbCr颜色空间以及YIQ颜色空间下,进行手写体文字图像聚类效果的分析比较,并结合空间域滤波增强与边缘检测技术提取出所需要的手写体文字信息。结果所选择研究对象在Lab颜色空间下对手写体文字具有较好的提取效果,有利于后续的文字识别。结论颜色空间聚类方法能有效避免灰度转换造成颜色信息丢失而引起的误判,在保证原有阈值分割算法快速、简单的前提下,能够对彩色图像进行更为准确的分割。     

Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for extracting the alphabets, while diagonal-based extraction methods are implemented in online systems. Moreover, our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text recognition. To this end, we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline mediums. Subsequently, we carried out detailed experiments for character recognition, as detailed in the results. The proposed CNN model outperformed as compared to previously published approaches.     

Arabic Named Entity Recognition: A BERT-BGRU Approach

Named Entity Recognition (NER) is one of the fundamental tasks in Natural Language Processing (NLP), which aims to locate, extract, and classify named entities into a predefined category such as person, organization and location. Most of the earlier research for identifying named entities relied on using handcrafted features and very large knowledge resources, which is time consuming and not adequate for resource-scarce languages such as Arabic. Recently, deep learning achieved state-of-the-art performance on many NLP tasks including NER without requiring hand-crafted features. In addition, transfer learning has also proven its efficiency in several NLP tasks by exploiting pretrained language models that are used to transfer knowledge learned from large-scale datasets to domain-specific tasks. Bidirectional Encoder Representation from Transformer (BERT) is a contextual language model that generates the semantic vectors dynamically according to the context of the words. BERT architecture relay on multi-head attention that allows it to capture global dependencies between words. In this paper, we propose a deep learning-based model by fine-tuning BERT model to recognize and classify Arabic named entities. The pre-trained BERT context embeddings were used as input features to a Bidirectional Gated Recurrent Unit (BGRU) and were fine-tuned using two annotated Arabic Named Entity Recognition (ANER) datasets. Experimental results demonstrate that the proposed model outperformed state-of-the-art ANER models achieving 92.28% and 90.68% F-measure values on the ANERCorp dataset and the merged ANERCorp and AQMAR dataset, respectively.     

Recognition of the meaningful dotted pattern from a dotted image

Abstract

Based upon the selective attention in human visual perception, a conceptual computation model performing the recognition of the meaningful dotted pattern from a dotted image will be presented. This consists of two major processes. A passive process performs images partition for the segmentation, and an active process performs distance computation for the pattern recognition. The principle of this model is illustrated by a set of Arabic numerals, and its further application is also pointed out.     

基于智能多代理的复杂场景钢坯字符切分算法

在生产线钢坯检测识别过程中,如何准确地切分生产线实际复杂场景下的钢坯端面字符是一个高度复杂的智能问题.为了解决这一复杂问题,本文提出了一种基于智能多代理者的字符切分处理方法,将分控制层中的字符区域分割与切分、区域合并、区域分裂、特征计算等功能子程序作为个体代理者,主控制层作为主控代理者对这些个体代理者根据具体需要进行统一分工协调,同时各子代理者的切分信息反馈给主控代理者作为分析、控制各子代理者的重要因素,进而完成钢坯号字符的精确切分.实验结果表明,本文提出的算法能对复杂场景中的钢坯字符完成精确的切分,具有良好的稳定性与准确性,解决了复杂场景中的钢坯字符准确切分的问题,为后续钢坯字符的识别提供了保证.