Recognition of IC chips via machine vision

Abstract

The purpose of this paper is to develop an automatic IC chip inspection and recognition system. This vision system can find the pins in an IC chip and then determine its major axis precisely. Based on the rules developed in this study, the system can successfully detect the label blocks printed on the IC surface. An algorithm for dividing merged characters within a block has also been established. The features of the characters are constructed from stroke relationship and their pattern profile. A fuzzy‐neural network is used to classify the symbols and determine the orientation of IC chips. Finally, the system can combine them into meaningful data and recognize the symbols.     

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。     

Spoken Indian language identification: a review of features and databases

Spoken language is one of the distinctive characteristics of the human race. Spoken language processing is a branch of computer science that plays an important role in human–computer interaction (HCI), which has made remarkable advancement in the last two decades. This paper reviews and summarizes the acoustic, phonetic and prosody features that have been used for spoken language identification specifically for Indian languages. In addition, we also review the speech databases, which are already available for Indian languages and can be used for the purposes of spoken language identification.     

Error analysis to improve the speech recognition accuracy on Telugu language

Speech is one of the most important communication channels among the people. Speech Recognition occupies a prominent place in communication between the humans and machine. Several factors affect the accuracy of the speech recognition system. Much effort was involved to increase the accuracy of the speech recognition system, still erroneous output is generating in current speech recognition systems. Telugu language is one of the most widely spoken south Indian languages. In the proposed Telugu speech recognition system, errors obtained from decoder are analysed to improve the performance of the speech recognition system. Static pronunciation dictionary plays a key role in the speech recognition accuracy. Modification should be performed in the dictionary, which is used in the decoder of the speech recognition system. This modification reduces the number of the confusion pairs which improves the performance of the speech recognition system. Language model scores are also varied with this modification. Hit rate is considerably increased during this modification and false alarms have been changing during the modification of the pronunciation dictionary. Variations are observed in different error measures such as F-measures, error-rate and Word Error Rate (WER) by application of the proposed method.     

导视空间中汉字识别设计

目的从认知心理学的研究视角,探究导视空间中汉字识别设计的基本规律及其在空间场域中的影响因素。方法采用定性研究的方法,分别从汉字字形结构、汉字正负形空间布局、汉字的中宫,以及汉字在空间场域中的阅读视角与阅读距离等影响因素展开深入的讨论与分析。结论导视空间中的汉字识别是一个复杂的认知心理过程,汉字识别要从空间场域的视觉传达特点出发,通常采用字形结构识别强度最高的泛黑体字,尽可能做到泛黑体字的空间正负形布局合理,并且汉字的中宫放松。尤其要考虑到汉字在空间场域中的阅读视角与阅读距离等影响因素,这一研究能为导视空间中的汉字识别设计提供一条有效的设计路径与方法。     

汉字字体类型与字体结构的易读性研究

目的探讨不同字体的易读性情况,为字体设计提供实证支持。方法实验采用8×5的被试内设计,研究字体类型和字体结构对汉字识别速度的影响。结果方正黑体、方正兰亭黑等无衬线字体的识别效果要好于宋体及其变式等衬线体;字体类型对汉字识别的效应还受到字体结构的影响,在全包围、半包围、独体和上下结构上,字体类型的作用更显著。结论在汉字识别的过程中,人们是以整字为单位进行加工的,而不考虑字的具体笔画和部件。字型的扁方设计、笔画的适当加粗以及大字面的字体设计有利于汉字的识别。     

一种基于视图和支持向量机的三维物体识别方法

为提高三维物体识别系统性能并减少计算复杂性,本文提出了一种基于视图的方法.首先从三维物体的二维视图中提取颜色矩、纹理特征和仿射不变矩.颜色矩对于物体的大小和姿态不敏感且性能稳健.纹理特征可区别形状相似但外观不同的物体.仿射不变矩在物体发生仿射形变下具有不变性.本文将上述各种特征组合为23个分量的特征向量,送入支持向量机进行训练并识别.基于两种公开的三维物体数据库COIL-100和ALOI测试了本文方法性能.当每物体训练视角为36个(视角间隔10°)时,在两个数据库上的实验都达到了100%的识别率.进一步减少训练视角数量也达到较满意的识别性能,优于文献中的方法.     

Real object recognition using moment invariants

Moments and functions of moments have been extensively employed as invariant global features of images in pattern recognition. In this study, a flexible recognition system that can compute the good features for high classification of 3-D real objects is investigated. For object recognition, regardless of orientation, size and position, feature vectors are computed with the help of nonlinear moment invariant functions. Representations of objects using two-dimensional images that are taken from different angles of view are the main features leading us to our objective. After efficient feature extraction, the main focus of this study, the recognition performance of classifiers in conjunction with moment-based feature sets, is introduced     

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.     

Recognition and interpretation of interacting and non-interacting features using spatial decomposition and Hamiltonian path search

Since it is a complex task to formalize the feature recognition problem explicitly, a large variety of systems has been developed. One of the problems these systems have to overcome is the recognition and interpretation of interacting features. A fair success has been achieved in surface based methods to recognize certain classes of interacting features. However the problem remains for general cases of interacting features. Recently much effort has been focused on the volumetric approach. We present here the current state of a volumetric feature recognition method. The system considers interacting features in prismoidal parts and it operates in two stages: (1) recognition of regions of interest: a spatial decomposition of the space bounded by a predefined circumscribing volume is performed. A ‘cell evaluated and directed adjacency graph’ is then established. This graph is traversed to identify cavity volumes. (2) interpretation: cavity volumes made up of more than one cell can be produced by different machining operations. A graph-based decomposition method and Hamiltonian path search are combined to generate interpretations which correspond to optimal machining. The system CEDAG developed in this work uses a cell-face directed graph and contrasts the face-edge and edge-vertex graphs encountered in most conventional graph-based recognition methods.