Arabic Handwriting Classification using Deep Transfer Learning Techniques

Arabic handwriting is slightly different from the handwriting of other languages; hence it is possible to distinguish the handwriting written by the native or non-native writer based on their handwriting. However, classifying Arabic handwriting is challenging using traditional text recognition algorithms. Thus, this study evaluated and validated the utilisation of deep transfer learning models to overcome such issues. Hence, seven types of deep learning transfer models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were used to determine the most suitable model for classifying the handwritten images written by the native or non-native. Two datasets comprised of Arabic handwriting images were used to evaluate and validate the newly developed deep learning models used to classify each model’s output as either native or foreign (non-native) writers. The training and validation sets were conducted using both original and augmented datasets. Results showed that the highest accuracy is using the GoogleNet deep learning model for both normal and augmented datasets, with the highest accuracy attained as 93.2% using normal data and 95.5% using augmented data in classifying the native handwriting.

E-Commerce Picture Text Recognition Information System Based on Deep Learning

For the accuracy requirements of commodity image detection and classification, the FPN network is improved by DPFM ablation and RFM, so as to improve the detection accuracy of commodities by the network. At the same time, in view of the narrowing of channels in the application of traditional MWI-DenseNet network, a new GTNet network is proposed to improve the classification accuracy of commodities.The results show that at different levels of evaluation indexes, the dpFPN-Netv2 algorithm improved by DPFM + RFM fusion has higher target detection accuracy than RetinaNet-50 algorithm and other algorithms. And the detection time is 52 ms, which is significantly lower than 90 ms required for RetinaNet-50 detection. In terms of target recognition, compared with the traditional MWI-DenseNet neural network, the computation amount of the improved MWI DenseNet neural network is significantly reduced under different shunt ratios, and the recognition accuracy is significantly improved. The innovation of this study lies in improving the algorithm from the perspective of target detection and recognition, so as to change the previous improvement that only can be made in a single way.

A Remote Calibration Device Using Edge Intelligence

Power system facility calibration is a compulsory task that requires in-site operations. In this work, we propose a remote calibration device that incorporates edge intelligence so that the required calibration can be accomplished with little human intervention. Our device entails a wireless serial port module, a Bluetooth module, a video acquisition module, a text recognition module, and a message transmission module. First, the wireless serial port is used to communicate with edge node, the Bluetooth is used to search for nearby Bluetooth devices to obtain their state information and the video is used to monitor the calibration process in the calibration lab. Second, to improve the intelligence, we propose a smart meter reading method in our device that is based on artificial intelligence to obtain information about calibration meters. We use a mini camera to capture images of calibration meters, then we adopt the Efficient and Accurate Scene Text Detector (EAST) to complete text detection, finally we built the Convolutional Recurrent Neural Network (CRNN) to complete the recognition of the meter data. Finally, the message transmission module is used to transmit the recognized data to the database through Extensible Messaging and Presence Protocol (XMPP). Our device solves the problem that some calibration meters cannot return information, thereby improving the remote calibration intelligence.

End-to-end platform evaluation for Spanish Handwritten Text Recognition

The task of automated recognition of handwritten texts requires various phases and technologies both optical and language related. This article describes an approach for performing this task in a comprehensive manner, using machine learning throughout all phases of the process. In addition to the explanation of the employed methodology, it describes the process of building and evaluating a model of manuscript recognition for the Spanish language. The original contribution of this article is given by the training and evaluation of Offline HTR models for Spanish language manuscripts, as well as the evaluation of a platform to perform this task in a complete way. In addition, it details the work being carried out to achieve improvements in the models obtained, and to develop new models for different complex corpora that are more difficult for the HTR task.

Digitising (Romanian) Cyrillic using Transkribus: new perspectives

In this paper we discuss the application of the software platform Transkribus (transkribus.eu), an AI-assisted tool for Handwritten Text Recognition (HTR), to 16th century Romanian manuscript and printed sources using Cyrillic scripts. After an overview of the basic functionality of the HTR technology and Transkribus, we discuss the Romanian and bilingual Slavonic-Romanian sources we used, give an insight on training specific and generic as well as smart (i.e. transliterating from Cyrillic into Latin script) models, evaluate their performance and discuss implications of HTR for philological research in the Digital Age. We conclude with an outlook on future research perspectives.

Smart Archive Generation Using Computer Vision, NLP and Big Data

Gaining insights from the dense network of interrelated documents involved in E&P projects requires experience, knowledge, and awareness about the existence of the required data. This framework aims to facilitate the decision-making process while consuming shorter time periods and lower costs, without sacrificing the accuracy of the data and decreasing the probability of human errors. The high complexity of E&P Projects results in a dense network of interrelated documents which are produced to cover the various aspects and details of the project. Gaining insights from old data requires experience, knowledge, and awareness about the existence of the required data. Accordingly, the knowledge accumulated over the time from various projects can be considered a key asset, since it can be leveraged to perform more informed decisions. This paper presents a framework that aim at capturing organizational knowledge locked in paper-based datasets and store it in a structured digital format that facilitates its retrieval and enables analyses which help uncover valuable insights. This research aims to generate valuable data from existing archives while causing minimal disturbance to existing business processes and workflows. The framework performs four main functions: image processing, text recognition, Data Analytics and Data storage. Initially the text recognition module; which is performs Image Processing to enhance the quality of the scanned files, and optical character recognition using LSTM which extracts the text contained in images. The Data Analytics Module, then cleanses and mines the extracted text using Big Data Analytics tools. Text Matching and searching is performed on the Spark Dataframe using regular expressions to identify different attributes and their different types. Finally, the data is stored in a SQL Database. In order to measure the workflow's accuracy a manual baseline was generated for a sample project. The accuracy is measured using field-level verification, since it was found to be the most fit-for-purpose, as it allows to measure the accuracy of the workflow on the level of each field.