Optimised CNN Architectures for Handwritten Arabic Character Recognition

Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.

Arabic Optical Character Recognition:A Review

This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingly.The study describes the characteristics of the Arabic language,different types of OCR systems,different stages of the Arabic OCR system,the researcher’s contributions in each step,and the evaluationmetrics for OCR.The study reviews the existing datasets for the Arabic OCR and their characteristics.Additionally,this study implemented some preprocessing and segmentation stages of Arabic OCR.The study compares the performance of the existing methods in terms of recognition accuracy.In addition to researchers’OCRmethods,commercial and open-source systems are used in the comparison.The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters.Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment,and few approaches were tested on pagelevel scripts.Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low,under 75%for printed text,and further improvement is needed.Moreover,most of the current approaches are offline OCR systems,and there is no remarkable contribution to online OCR systems.

Review of Optical Character Recognition for Power System Image Based on Artificial Intelligence Algorithm

Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effect of OCR based on Artificial Intelligence(AI)algorithms,in which the different AI algorithms for OCR analysis are classified and reviewed.Firstly,the mechanisms and characteristics of artificial neural network-based OCR are summarized.Secondly,this paper explores machine learning-based OCR,and draws the conclusion that the algorithms available for this form of OCR are still in their infancy,with low generalization and fixed recognition errors,albeit with better recognition effect and higher recognition accuracy.Finally,this paper explores several of the latest algorithms such as deep learning and pattern recognition algorithms.This paper concludes that OCR requires algorithms with higher recognition accuracy.

基于OCR和Pydicom的PACS数据库数据丢失后的应急与恢复研究

目的在影像归档和通信系统(Picture Archiving and Communication System,PACS)数据库文件丢失或损坏后,实现影像资料和PDF报告关键信息的快速识别和重组,供患者回诊使用。方法利用基于深度学习的光学字符识别技术和Pydicom技术分别读取PDF和DCOM文件中的基本信息,重新建立起患者、影像、报告三者之间的联系,并将关联数据写入数据库。结果经抽样验证,该方法识别同类图像精度的准确度、精准度及召回率均为100%,综合指标F1值为1,在不同组别独立样本间的识别精度表现出一致性。平均每份报告识别时间约为0.14 s(t=-1.005,P=0.315),说明不同组别独立样本间的识别时间表现出一致性。结论该方法的使用能有效缩短数据库故障后患者等待时长,能够在短时间内恢复医疗秩序,可用于PACS数据库数据丢失后的应急处置,也为PACS的数据整合提供依据,为医学影像数据恢复和数据整合提供一种新思路。

基于Tesseract-OCR的农村房地一体归档系统研究

农村房地一体档案是对农村宅基地、集体建设用地使用权及房屋所有权进行确权登记的重要依据,将签章后的纸质档案转为电子档案进行存储对不动产权证书办理具有重要意义。由于目前缺乏能识别档案内容并进行分类归档的工具,设计并实现了基于Tesseract-OCR的农村房地一体归档系统。使用光学字符识别(Optical Character Recognition,OCR)对档案扫描图像进行识别,训练校正字库,提取图像中的文字信息,实现档案资料的分类存储。运用四川省某县的部分房地一体档案进行系统测验,应用结果表明,系统的识别归档准确率为96.5%,能满足房地一体档案归档需求,降低了人工识别归档的繁琐性,极大提高了归档的工作效率,提升了档案分类的准确度。

基于OCR模型的通信机房图片归档系统设计

目前通信机房图片归档,人工操作占据了主导地位,然而这种方式存在效率低、易出错等缺陷。在此背景下,文章提出了一种基于光学字符识别(Optical Character Recognition,OCR)模型的通信机房图片归档系统。该系统通过自动识别图片中的文字信息,分析图片所属的机房位置,进而按照机柜位置分类归档图片,实现自动化管理。经过测试,该系统的归档准确率达到了98%以上,显著提高了通信机房图片归档的效率。

A Fast Recognition System for Isolated Printed Characters Using Center of Gravity and Principal Axis

The purpose of this paper is to propose a new multi stage algorithm for the recognition of isolated characters. It was similar work done before using only the center of gravity (This paper is extended version of “A fast recognition system for isolated printed characters using center of gravity”, LAP LAMBERT Academic Publishing 2011, ISBN: 978-38465-0002-6), but here we add using principal axis in order to make the algorithm rotation invariant. In my previous work which is published in LAP LAMBERT, I face a big problem that when the character is rotated I can’t recognize the character. So this adds constrain on the document to be well oriented but here I use the principal axis in order to unify the orientation of the character set and the characters in the scanned document. The algorithm can be applied for any isolated character such as Latin, Chinese, Japanese, and Arabic characters but it has been applied in this paper for Arabic characters. The approach uses normalized and isolated characters of the same size and extracts an image signature based on the center of gravity of the character after making the character principal axis vertical, and then the system compares these values to a set of signatures for typical characters of the set. The system then provides the closeness of match to all other characters in the set.

CHARACTER DETECTION AND RECOGNITION SYSTEM OF BEER BOTTLES

An optical imaging system and a configuration characteristic algorithm are presented to reduce the difficulties in extracting intact characters image with weak contrast, in recognizing characters on fast moving beer bottles. The system consists of a hardware subsystem, including a rotating device, CCD, 16 mm focus lens, a frame grabber card, a penetrating lighting and a computer, and a software subsystem. The software subsystem performs pretreatment, character segmentation and character recognition. In the pretreatment, the original image is filtered with preset threshold to remove isolated spots. Then the horizontal projection and the vertical projection are used respectively to retrieve the character segmentation. Subsequently, the configuration characteristic algorithm is applied to recognize the characters. The experimental results demonstrate that this system can recognize the characters on beer bottles accurately and effectively; the algorithm is proven fast, stable and robust, making it suitable in the industrial environment.

Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

In today’s digital era,the text may be in form of images.This research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary creation.SVM rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords features.The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features.The second goal is to maintain or improve the classification system’s performance.The experiment included news articles from various genres,such as Bollywood,politics,and sports.The proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human summaries.The evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.

基于OCR的智能电表缺陷检测系统

智能电表外观检测是智能电表检定流水线中重要环节,传统检定方法主要依靠人工,不仅费时而且误检率高。由于真实含缺陷样本数量稀少,无法构建数据充足的缺陷样本数据集,因此文中基于OCR检测正常字符,对未通过检测的字符进行缺陷检测。首先利用自建数据集训练一个能检测电气符号的OCR模型,并对每一类电表建立一个标准模板。针对文字检测漏检问题提出了通过模板信息与文字检测结果求取遗漏的待检测区域算法。针对缺陷检测容易误检漏检等问题设计了一个多维度缺陷检测算法。实验结果表明:设计的文本区域分割算法切分准确率能达到100%,OCR识别准确率为96.4%,缺陷检测准确率为98.3%,在RTX3060显卡条件下平均检测速度为0.524 s/张。所设计的方法满足工业检定流水线的检测精度与检测速度需求。

Baseline Isolated Printed Text Image Database for Pashto Script Recognition

The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages.Moreover,the absence of a standard publicly available dataset for several low-resource lan-guages,including the Pashto language remained a hurdle in the advancement of language processing.Realizing that,a clean dataset is the fundamental and core requirement of character recognition,this research begins with dataset generation and aims at a system capable of complete language understanding.Keeping in view the complete and full autonomous recognition of the cursive Pashto script.The first achievement of this research is a clean and standard dataset for the isolated characters of the Pashto script.In this paper,a database of isolated Pashto characters for forty four alphabets using various font styles has been introduced.In order to overcome the font style shortage,the graphical software Inkscape has been used to generate sufficient image data samples for each character.The dataset has been pre-processed and reduced in dimensions to 32×32 pixels,and further converted into the binary format with a black background and white text so that it resembles the Modified National Institute of Standards and Technology(MNIST)database.The benchmark database is publicly available for further research on the standard GitHub and Kaggle database servers both in pixel and Comma Separated Values(CSV)formats.

基于自定义模板的OCR技术及应用

信息资料电子化工作中,基于深度学习的光学字符识别(OCR)技术虽然是一种有效的文字识别方法,但也存在人工干预多以及结构化数据识别困难等问题。为了提高应用系统自动化程度以及满足用户定制化需求,提出了一种基于自定义模板的OCR技术。实际应用表明,基于自定义模板的OCR技术既可减少人工干预又可解决结构化数据识别问题,有助于提高信息加工与处理效率。

基于OCR的标书文件信息获取技术应用研究

电子评标过程中,由于目前的辅助招评标系统在智能化程度方面有所欠缺,在评标效率、准确率等方面仍有提升进步的区间。例如,在获取招投标文件图片信息中,现有的辅助招评标系统识别效果较差。为解决现有问题,提出了一种通过使用光学字符识别(Optical Character Recognition,OCR)技术获取招投标文件内容,并对上传图片进行灰度值、图像预处理。该方法可大幅度增强系统智能辅助招评标功能,使用公章检测算法判断招投标文件中公章使用情况,划分标书文字块,从而缩短评标时间,减轻评审标书的工作强度,解决了评标过程中的评审不公正、评标效率低等问题,使招投标项目的评标更加公平、公正、公开。

基于深度学习的OCR概述和对学生影响的思考

文章从一个综合性的视角对光学字符识别技术进行了介绍首先对光学字符识别技术的背景以及国内外的发展现状进行了概述包括对应用领域和市场前景的探讨其次详细介绍了图像文本检测的过程并采用深度学习方法对一些图文检测网络的架构与特征进行了分析同时介绍了几个图文识别数据集最后分析了光学字符识别技术对学生的影响并提出了相应的建议和总结.

航空发动机叶片装配执行过程智能检测及AR引导

为了提高航空发动机叶片装配执行过程的作业智能化程度,本文提出一种航空发动机叶片装配执行过程智能检测及AR引导方法,该方法包括叶片编码识别、物料AR出入库和齐套摆放过程状态检测3个环节。针对航空发动机叶片物料缺乏自动化识别和智能化纠错的问题,搭建基于编码识别的叶片物料管理架构,提出基于图像处理的叶片编码图像前处理增强操作,并利用贝叶斯纠错对识别结果进行正误判断和纠错校正的后处理操作,提高了叶片编码识别准确率;在物料人工出入库环节,利用AR增强可视化信息辅助用户快速执行作业任务,降低了叶片物料选取作业的时间;针对叶片物料齐套准备过程,构建了基于检测比对的防错纠错系统,避免发生人为错误。所提叶片装配执行过程智能检测及AR增强辅助引导方法可以有效减少人力物力和时间消耗,在推动航空发动机迈向智能化和自动化生产上起到技术支撑作用。

基于机器视觉的手写钢板号图像增强及矫正算法研究与应用

钢板号的正确识别检查是实现生产线自动化生产的重要基础条件之一。近年来,许多生产线在备料位置配备了喷印机用于自动标记物料编号。喷印的字迹清晰且耐高温,在没有涂抹的情况下使用钢板号识别设备可以实现接近100%的识别率。然而,由于喷印设备故障或受限于资金和空间等原因,有时无法安装喷印设备,只能依赖人工手写的方式在钢板表面标记编号。与喷印编号相比,手写编号存在书写随意、连笔、字迹歪斜扭曲等复杂情况,这些因素限制了识别系统的准确性。鉴于识别效果较差,通常需要依赖人工目测来辅助识别,从而影响了物料跟踪自动化的实施效果。为了提升手写钢板号的识别效果,对传统机器学习光学字符识别(OCR)文本区域检测算法进行改进研究,并针对手写钢板号的特征,提出一种图像增强和扭曲矫正处理的算法。应用结果表明,该算法可以改善手写钢板号的图像质量和形状,提高识别的准确性。该研究旨在提升手写钢板号识别效果,以解决自动化生产中的难题。通过图像增强和矫正处理,使识别系统更好地处理手写钢板号,推动物料跟踪的自动化实施。

利用OCR识别技术实现视频中文字的提取

为了在视频图像中进行字幕信息的实时提取,提出了一套简捷而有效的方法。首先进行文字事件检测,然后进行边缘检测、阈值计算和边缘尺寸限制,最后依据文字像素密度范围进一步滤去非文字区域的视频字幕,提出的叠加水平和垂直方向边缘的方法,加强了检测到的文字的边缘;对边缘进行尺寸限制过滤掉了不符合文字尺寸的边缘。应用投影法最终确定视频字幕所在区域。最后,利用OCR识别技术对提取出来的文字区域进行识别,完成视频中文字的提取。以上方法的结合保证了提出算法的正确率和鲁棒性。

新的基于统计熵功率的OCR算法及其DMCU实现

使用摄像头进行文字识别最大的问题在于图像抖动。为了有效地消除图像抖动并正确实现文字识别,提出了一种基于统计熵功率的新的识别算法。这种方法将采集到的数据作为随机信号处理。实验证明,此算法计算复杂度低,识别率高,适用于低成本嵌入式系统,在中国台湾俊亿公司24MHZ16位DMCU嵌入式系统上,获得了94%以上的正确识别率。

基于OCR识别技术的自动阅卷系统的研究

利用光学字符识别(OCR)技术,设计的自动阅卷系统,不仅能够实现对答题卡的自动阅卷工作,还可以完成成绩的统计和成绩单的打印工作,已在实际中得到应用。结果表明该系统提高了考试阅卷工作的效率,与一般的自动阅卷系统相比,有着独特的优点,应用前景广阔。