Text Recognition Github

While optical character recognition (OCR) in document images is well studied and many commercial tools are available, the detection and recognition of text in natural images is still a challenging problem, especially for some more complicated character sets such as Chinese text. Optical Character Recognition (OCR) is part of the Universal Windows Platform (UWP), which means that it can be used in all apps targeting Windows 10. js also lets you to add voice commands to your website easily, build your own Google Now, Siri or Cortana ! Github repository Read the documentation Get Artyom. Words are important in speech recognition because they restrict combinations of phones significantly. picture taken with phone) from a user and process it in order to return the text contained in it. how to crop each and every word containing in the image?. Optical Character Recognition (OCR) gives a computer the ability to read text that appears in an image, letting applications make sense of signs, articles, flyers, pages of text, menus, or any. Skip to content. Text Recognition in Natural Images in Python. See the ML Kit quickstart sample on GitHub for an example of this API in use, or try the codelab. (Machine Learning Vol 6 #2 March 91) Papers That Cite This Data Set 1: Xiaoli Z. Wu∗ Adam Coates Andrew Y. GitHub; Control anything with your voice Learn how to build your own Jasper. Most importantly, compared with autoregressive Transformer TTS, our model speeds up the mel-spectrogram generation by 270x and the end-to-end speech synthesis by 38x. The open source GNU/Linux speech recognition program that uses Google's voice APIs on the back-end is now called Palaver. (I have done well with some APIs from web services. Source code. In this post we will focus on explaining how to use OCR on Android. Analyse video in near real time. Restriction: In addition to the LaTeX command the unlicensed version will copy a reminder to purchase a license to the clipboard when you select a symbol. In this quickstart, you will extract printed text with optical character recognition (OCR) from an image by using Computer Vision's REST API. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. STN-OCR, a single semi-supervised Deep Neural Network(DNN), consist of a spatial transformer network — which is used to detected text regions in images, and a text recognition network — which…. The CLI TTS utilities encourage experimentation and allow you to store an audio file that is returned from the server based on text and the given language. awesome-deep-text-detection-recognition. Speech-to-text from Azure Speech Services, also known as speech-to-text, enables real-time transcription of audio streams into text that your applications, tools, or devices can consume, display, and take action on as command input. A noise removal function is underway. See the complete profile on LinkedIn and discover Farrukh’s. This project hosts the samples for the Microsoft Cognitive Services Speech SDK. You may get poor results if your input image contains a few regions of text or the text is located in a cluttered scene. Google Cloud Speech API, Micro. The Mobile Vision Text API gives Android developers a…. End-to-End Text Recognition with Convolutional Neural Networks Tao Wang∗ David J. Amazon Rekognition is always learning from new data, and we are continually adding new labels and facial recognition features to the service. More on the way. Back to top. The Text Analytics' entities endpoint supports both named entity recognition (NER) and entity linking. In return, OCRopus was also used for automatic text recognition in Google Book Search. This article focuses on performing speech recognition via the REST API. It can be used on servers and in desktop applications. Cluster Ensembles for High Dimensional Clustering: An Empirical Study. The Mozilla deep learning architecture will be available to the community, as a foundation. End-to-End Recognition: Reading Text seek word level candidates using multiple region proposal methods (EdgeBoxes, ACF detector) refine bounding boxes of words by regression perform word recognition using very large convolutional neural networks Jaderberg et al. Offline Handwritten Text Recognition (HTR) systems transcribe text contained in scanned images into digital text, an example is shown in Fig. Optical Character Recognition (OCR) is part of the Universal Windows Platform (UWP), which means that it can be used in all apps targeting Windows 10. Anyline is a mobile OCR SDK, which enables you to scan numbers and short text within your application. See the ML Kit quickstart sample on GitHub for an example of this API in use, or try the codelab. js, drop it in your html, and start adding commands. Skip to content. * @param gcsDestinationPath The path to the remote file on Google Cloud Storage to store the * results on. This repository contains the code for the OCR Project I'm working on as part of Data Science Retreat (Berlin). We propose RARE (Robust text recognizer with Automatic REctification), a recognition model that is robust to irregular text. When combined together these methods can be used for super fast, real-time object detection on resource constrained devices (including the Raspberry Pi, smartphones, etc. CMUSphinx Documentation. sentdex 187,600 views. Text extraction model. Once detected, the recognizer then determines the actual text in each block and segments it into lines and words. To cleanup, here is the list. A general list of resources to image text localization and recognition 场景文本位置感知与识别的论文资源与实现合集 シーンテキストの位置認識と識別のための論文リソー…. and allow full-text or key-word search. picture taken with phone) from a user and process it in order to return the text contained in it. We will build a Neural Network (NN) which is trained on word-images from the IAM dataset. This document is also included under reference/library-reference. Neuroph OCR - Handwriting Recognition is developed to recognize hand written letter and characters. Besides, artyom. Want a Mac app? Lucky you. We are now ready to perform text recognition with OpenCV!. - clovaai/deep-text-recognition-benchmark GitHub is home to over 36 million. text_recognition. Text Recognition in Natural Images in Python. The scope of computer vision is huge. The MNIST dataset, which comes included in popular machine learning packages, is a great introduction to the field. Automated recognition of documents, credit cards, recognizing and translating signs on billboards — all of this could save time for collecting and processing data. yang, meinel}@hpi. Next, we'll develop a simple Python script to load an image, binarize it, and pass it through the Tesseract OCR system. Optical character recognition (OCR) refers to the process of automatically identifying from an image characters or symbols belonging to a specified alphabet. NET/WinForms/ASP. There may be a maximum length of the text, it may be limited to 32,767 characters. Score is F1-score for localization task. Image and text recognition (MNIST and word2vec) Viswanath Puttagunta of Linaro provided an overview of neural network basics (weights, biases, gating functions, etc. According to the tracked word videos starts to play in Augmented Reality. Microsoft Recognizers Text Overview. Classifying e-commerce products based on images and text Sun 26 June 2016 The topic of this blog post is my project at Insight Data Science , a program that helps academics, like myself (astrophysicist), transition from academia into industry. Emotion can be from the frequency of voice or from the speech. Now anyone can access the power of deep learning to create new speech-to-text functionality. The title and body will stack on top of each other on account of the literal newlines:. Build smart apps and services that speak to users naturally with the Text to Speech service. Score is F1-score for localization task. text lines, a novel line follower network that incrementally follows and preprocesses lines of (perhaps curved) text into dewarped images suitable for recognition by a CNN-LSTM network. Deep learning is hot, and rightfully so: it has made breakthroughs across different fields, such as image classification, object detection and segmentation, machine translation, and speech. And if the system only has to handle left-aligned text, then it will not learn any other type of alignment. Document text recognition is available only as a cloud-based model. This demo project contains in addition other parts, like accessing the camera, handling bitmaps, making a camera focus box. GitHub; Control anything with your voice Learn how to build your own Jasper. This is to simulate real-world lighting variation. Analyse images to detect embedded text, generate character streams and enable searching. [View Context]. I've been kept busy with my own stuff, too. stringify() in function send():success, do this in function setResponse() so that val for the below is an Object. This demo requires getUserMedia and WebGL. speech-recognition. Wu∗ Adam Coates Andrew Y. Object detection with deep learning and OpenCV. Using the library for real-time recognition implies using bleeding-edge Web technologies that really are just emerging. Check out these five text recognition tools to get. Chinese handwriting recognition: Select language: With this tool you can draw a Chinese character which will be recognized. This page contains collaboratively developed documentation for the CMU Sphinx speech recognition engines. In the following demo, we trigger the voice recognition by using the start method when the user submits the form. You can also dictate and edit your text results right away, and continue dictating. Test database. Papers are sorted by published date. Audio Samples. See the overview for a comparison of the cloud and on-device models. Before I came to Adelaide, I was a visiting student at MMLAB of the Chinese University of Hong Kong at Shenzhen under the supervision of Dr. Companies sometimes exchange documents (contracts for instance) with personal information. js is a pure-javascript version of Antonio Diaz Diaz's Ocrad project, automatically converted using Emscripten. component_rects – If provided the method will output a list of Rects for the individual text elements found (e. Named Entity Recognition (NER) on unstructured text has numerous uses. Kaldi's code lives at https://github. text matching as image recognition lies in the different rep-resentations of text and image: the former are two 1D (one-dimensional) word sequences while the latter is typically a 2D pixel grid. Back to top. 0 June 2019 we released. In this tutorial we will use Google Speech Recognition Engine with Python. Speech SDK Release 1. Cluster Ensembles for High Dimensional Clustering: An Empirical Study. We also study speech retrieval and cross-language information retrieval. Text Recognition plat_ios plat_android With ML Kit's text recognition APIs, you can recognize text in any Latin-based language ( and more, with Cloud-based text recognition ). It can be downloaded for all mobile platforms. STN-OCR, a single semi-supervised Deep Neural Network(DNN), consist of a spatial transformer network — which is used to detected text regions in images, and a text recognition network — which…. Please, listen carefully: If you want to code by yourself, which is great, you cannot ask "is there any code for handwriting recognition?". The SpeechRecognition interface of the Web Speech API is the controller interface for the recognition service; this also handles the SpeechRecognitionEvent sent from the recognition service. Get results from your videos faster. Speechnotes lets you type at the speed of speech (slow & clear speech). You either need code, or need to write it by yourself. it is a method to help computers recognize different textures or characters. You can also access other Pocketsphinx methods that are wrapped in Java classes in swig. Text recognition can automate tedious data entry for credit cards, receipts, and business cards. The proposal of the project was to implement an end-to-end text recognition pipeline. Google Speech To Text API. The scope of computer vision is huge. The novelties include: training of both text detec-tion and recognition in a single end-to-end pass, the struc-ture of the recognition CNN and the geometry of its input layer that preserves the aspect of the text and adapts its res-olution to the data. Weilin Huang and Prof. May 19, 2018. Full support for Chinese, English, French, Spanish, Portuguese, and German. It should work (as for 03. This is a follow-up post of my tutorial on Hand Gesture Recognition using OpenCV and Python. This blog post is divided into three parts. I have had to use the GetVisibleText and GetTextLocation methods to automate our application and this worked fine in Windows XP and prior but in Windows 7 it looks like the API text recognition is no longer supported. Our speech recognition technologies combine multiple APIs to produce the text output. This demo is open source, and is hosted on GitHub. Abstract: Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. The MNIST dataset, which comes included in popular machine learning packages, is a great introduction to the field. You will first work to run the text recognition locally on the device, and then follow that up with running in the cloud. IC is shorts for ICDAR. Learn more. A full discussion would fill a book, so I won't bore you with all of the technical details here. It can be used on servers and in desktop applications. This tutorial demonstrates how to upload image files to Google Cloud Storage, extract text from the images using the Google Cloud Vision API, translate the text using the Google Cloud Translation API, and save your translations back to Cloud Storage. This tutorial will show you how to build a basic speech recognition network that recognizes ten different words. A general list of resources to image text localization and recognition 场景文本位置感知与识别的论文资源与实现合集 シーンテキストの位置認識と識別のための論文リソー…. In this tutorial we are going to implement Google Speech Recognition in our Android Application which will convert user's voice to text and it will display it in TextView. Get results from your videos more quickly. We will also learn how to build a near state-of-the-art deep neural network model using Python and Keras. Therefore text lines are extracted from the input document that should then be recognized. The tutorial is intended for developers who need to apply speech technology in their applications, not for speech recognition researchers. This demo requires getUserMedia and WebGL. This is synthetically generated dataset which we found sufficient for training text recognition on real-world images. It should work (as for 03. In scikit-learn, for instance, you can find data and models that allow you to acheive great accuracy in classifying the images seen below:. I show you. So, we create a project in firebase console. Both desktop and mobile. Check out these five text recognition tools to get. You can also visit annyang on GitHub, and read the full API documentation. Ng Stanford University, 353 Serra Mall, Stanford, CA 94305 {twangcat, dwu4, acoates, ang}@cs. You need to dump speech utterances into wav files, write the reference text file and use decoder to decode it. [View Context]. how to crop each and every word containing in the image?. Score is F1-score for localization task. ognize text from natural images, in a semi-supervised way. [2015-CoRR] An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition paper code github; AI Lab, Stanford [2012-ICPR, Wang] End-to-End Text Recognition with Convolutional Neural Networks paper code SVHN Dataset. Speech SDK Release 1. Our end-to-end text spotting pipeline uses a combination of high recall region proposal methods, followed by a cascade of classifiers and a bounding box regressor. awesome-deep-text-detection-recognition. SEE is a network that integrates and jointly learns a spatial transformer network, which can learn to detect text regions in an image, and a text recognition network that takes the iden-tified text regions and recognizes their textual content. The novelties include: training of both text detec-tion and recognition in a single end-to-end pass, the struc-ture of the recognition CNN and the geometry of its input layer that preserves the aspect of the text and adapts its res-olution to the data. Learn more about Teams. Check out these five text recognition tools to get. Speech recognition for Asterisk Speech recognition script for Asterisk that uses Cloud Speech API by Google. In the first part of this tutorial, we'll discuss what a seven-segment display is and how we can apply computer vision and image processing operations to recognize these types of digits (no machine learning required!). Text Recognition plat_ios plat_android With ML Kit's text recognition APIs, you can recognize text in any Latin-based language ( and more, with Cloud-based text recognition ). On-device or in the cloud. This is synthetically generated dataset which we found sufficient for training text recognition on real-world images. GitHub Gist: instantly share code, notes, and snippets. Speechnotes lets you type at the speed of speech (slow & clear speech). Spoken Language Processing by Acero, Huang and others is a good choice for that. Deep Learning Based Large Scale Handwritten Devanagari Character Recognition Ashok Kumar Pant (M. There is an open source OCR library that supports android: Tesseract. This is to simulate real-world lighting variation. The script utilizes the EAST text detector to find regions of text in the image and then takes advantage of Tesseract v4 for recognition. Audio Quality. It provides a quick and easy API to convert the speech recordings into text with the help of CMUSphinx acoustic models. Anyline is a mobile OCR SDK, which enables you to scan numbers and short text within your application. Both desktop and mobile. Code here: https://github. NET OCR APIs for accurate and fast text recognition. In this tutorial, we will learn how to recognize handwritten digit using a simple Multi-Layer Perceptron (MLP) in Keras. CMUSphinx is an open source speech recognition system for mobile and server applications. It extracts text from more than a billion public Facebook and Instagram images and video frames (in a wide variety of languages), daily and in real time, and inputs it into a text recognition model that has been trained on classifiers to understand the context of the text and the image together. Back to top. You can also access other Pocketsphinx methods that are wrapped in Java classes in swig. Github : http Text Detection and Recognition Tutorial In Vuforia and Unity 3D This Tutorial shows you how to implement Text Recognition in Vuforia within the Unity 3D environment. Neuroph OCR - Handwriting Recognition is developed to recognize hand written letter and characters. In this quickstart, you will extract printed text with optical character recognition (OCR) from an image by using Computer Vision's REST API. If you want to experiment with using it for speech recognition, you'll want to check out [Silicon Valley Data Science's] GitHub repository which promises you a fast setup for a speech. Ml Kit package. By Jackie Dove, The app's handwriting OCR (optical character recognition) engine extracts text from paper documents, like letters, school notes, meeting. Translate it to other. Get results from your videos more quickly. It should work (as for 03. Machine Learning for Better Accuracy. Finally, we describe a synthetic data generation process that dynamically provides the many training examples needed for accurate recognition without overfitting. Integrating Google ML Kit in iOS for Face Detection, Text Recognition and Many More: Google introduced ML Kit at Google IO this year, and it's also good at text recognition. If you are a researcher, it's recommended to start with a textbook on speech technologies. Spoken Language Processing by Acero, Huang and others is a good choice for that. CMUSphinx Documentation. Text recognition is the way of identifying the text in pictures and video streams and perceiving the content from there. Papers are sorted by published date. 2018-09-23 01:07 Regina Obe * [r16833] Prep for 2. speech recognition. This document is also included under reference/library-reference. Learn Python online: Python tutorials for developers of all skill levels, Python books and courses, Python news, code examples, articles, and more. awesome-deep-text-detection-recognition. SPEECH RECOGNITION WITH DEEP RECURRENT NEURAL NETWORKS Alex Graves, Abdel-rahman Mohamed and Geoffrey Hinton Department of Computer Science, University of Toronto ABSTRACT Recurrent neural networks (RNNs) are a powerful model for sequential data. Optical character recognition (OCR) is used to digitize written or typed documents, i. This tutorial shows how to track words in text. Following instructions in this page will help you to: Install ORK either from precompiled ROS packages or from source code. js is a pure-javascript version of Antonio Diaz Diaz's Ocrad project, automatically converted using Emscripten. Text recognition (optical character recognition) with deep learning methods. So, we create a project in firebase console. Text Recognition plat_ios plat_android With ML Kit's text recognition APIs, you can recognize text in any Latin-based language ( and more, with Cloud-based text recognition ). Chinese handwriting recognition: Select language: With this tool you can draw a Chinese character which will be recognized. So my question here is. When text is taken verbatim from Groner’s memo, it will be rendered in an alternative font. CliNER is designed to follow best practices in clinical concept extraction. Try instantly, no registration required. From face recognition to emotion recognition, to even visual gas leak detection comes under this category. STN-OCR, a single semi-supervised Deep Neural Network(DNN), consist of a spatial transformer network — which is used to detected text regions in images, and a text recognition network — which…. As you know, one of the more interesting areas in audio processing in machine learning is Speech Recognition. Chinese handwriting recognition: Select language: With this tool you can draw a Chinese character which will be recognized. The tutorial is intended for developers who need to apply speech technology in their applications, not for speech recognition researchers. The proposal of the project was to implement an end-to-end text recognition pipeline. edu Dan Shiferaw shiferaw@stanford. Optical character recognition (OCR) is used to digitize written or typed documents, i. speech recognition. The script utilizes the EAST text detector to find regions of text in the image and then takes advantage of Tesseract v4 for recognition. Process or edit it. Text to speech (TTS) and automatic speech recognition (ASR) are two dual tasks in speech processing and both achieve impressive performance thanks to the recent advance in deep learning and large amount of aligned speech and text data. The framework. Currently in beta status. End-to-End Text Recognition with Convolutional Neural Networks Tao Wang∗ David J. All of the audio samples use WaveGlow as vocoder. The "hello world" of object recognition for machine learning and deep learning is the MNIST dataset for handwritten digit recognition. With OCR you can extract text and text layout information from images. handong1587's blog. You do not need to play with unknown values, the first thing you should do is to collect a database of test samples and measure the recognition accuracy. Text Detection. pdf For text recognition I used the tesseract-ocr http. GitHub Gist: instantly share code, notes, and snippets. The Text Analytics' entities endpoint supports both named entity recognition (NER) and entity linking. I got my master degree at School of Remote Sensing and Information Engineering at Wuhan University supervised by Prof. This tutorial will show you how to build a basic speech recognition network that recognizes ten different words. See the ML Kit quickstart sample on GitHub for an example of this API in use, or try the codelab. It may be the case that the. Try instantly, no registration required. Also do not JSON. GitHub Gist: instantly share code, notes, and snippets. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. You can try out our text spotting pipeline applied to image retrieval. Abstract This project seeks to classify an individual handwritten word so that handwritten text can be translated to a digi-tal form. We will build a Neural Network (NN) which is trained on word-images from the IAM dataset. This is synthetically generated dataset which we found sufficient for training text recognition on real-world images. The whole running project can be downloaded from here. Process or edit it. Speech library for speech-to-text, i. The IBM Watson Speech to Text service uses speech recognition capabilities to convert Arabic, English, Spanish, French, Brazilian Portuguese, Japanese, Korean, German, and Mandarin speech into text. Once the text is identified, the recognizer at that point decides the real content in each square and fragments it into lines and words. Text Recognition in Natural Images in Python. NET OCR Library for C#/VB. This page contains collaboratively developed documentation for the CMU Sphinx speech recognition engines. See also the audio limits for streaming speech recognition requests. annyang plays nicely with all browsers, progressively enhancing browsers that support SpeechRecognition, while leaving users with older browsers unaffected. A general list of resources to image text localization and recognition 场景文本位置感知与识别的论文资源与实现合集 シーンテキストの位置認識と識別のための論文リソー…. Amazon Rekognition is a simple and easy to use API that can quickly analyze any image or video file stored in Amazon S3. Our speech recognition technologies combine multiple APIs to produce the text output. We have built a dictionary of millions of different possible entities, which we can rapidly lookup in your text using our matching engine. Creating A Text Generator Using Recurrent Neural Network 14 minute read Hello guys, it's been another while since my last post, and I hope you're all doing well with your own projects. With OCR you can extract text and text layout information from images. The component tree of the image is extracted by a threshold increased step by step from 0 to 255, incrementally computable descriptors (aspect_ratio, compactness, number of holes, and number of horizontal crossings) are computed for each ER and used as features for a classifier which estimates the class-conditional probability P(er|character). Entity Linking and Named Entity Recognition. Text Detection. Deep Learning for Logo Recognition, Part I. Speech library for speech-to-text, i. GitHub Gist: instantly share code, notes, and snippets. The ocr function provides an easy way to add text recognition functionality to a wide range of applications. Abstract This project seeks to classify an individual handwritten word so that handwritten text can be translated to a digi-tal form. Optical Character Recognition (OCR) gives a computer the ability to read text that appears in an image, letting applications make sense of signs, articles, flyers, pages of text, menus, or any. Zinnia: Online hand recognition system with machine learning. A React component for displaying text. See the TensorFlow Module Hub for a searchable listing of pre-trained models. ) Institute of Engineering Pulchowk, Nepal Email: gyawali. Deep learning is hot, and rightfully so: it has made breakthroughs across different fields, such as image classification, object detection and segmentation, machine translation, and speech. In the following demo, we trigger the voice recognition by using the start method when the user submits the form. Sign in Sign up Instantly share code, notes, and. This tutorial demonstrates how to upload image files to Google Cloud Storage, extract text from the images using the Google Cloud Vision API, translate the text using the Google Cloud Translation API, and save your translations back to Cloud Storage. Once the text is identified, the recognizer at that point decides the real content in each square and fragments it into lines and words. However, the lack of aligned data poses a major practical problem for TTS and ASR on low-resource languages. Once recognized the text of the image, it can be used to: Save it to storage. Take photos of text instead of copying to save time and effort. In this article, i will present an OCR android demo application, that recognize words from a bitmap source. This is the statement made on HP's support site. When text is taken verbatim from Groner's memo, it will be rendered in an alternative font. Google Speech To Text API. We dispatch in main queue, first remove all the existing text layers and then add new text layers for new recognized text. The experiments on standard benchmarks, including the IIIT-5K, Street View Text and ICDAR datasets, demonstrate the superiority of the proposed algorithm over the prior arts. [View Context]. It's optimized for text-heavy images (such as documents that have been digitally scanned) and for images with a lot of visual noise. Ng Stanford University, 353 Serra Mall, Stanford, CA 94305 {twangcat, dwu4, acoates, ang}@cs. 2018-09-23 01:07 Regina Obe * [r16833] Prep for 2. Text Recognition plat_ios plat_android With ML Kit's text recognition APIs, you can recognize text in any Latin-based language ( and more, with Cloud-based text recognition ). Hi! To add speech response add the following piece of code to function setResponse(), and let synth be a global var. Text Recognition is the process of detecting and recognising of textual information in images, videos, documents and other sources. Further information on all our systems can be found in the reports. Then calculate WER using the word_align. component_text – If provided the method will output a list of text strings for the recognition of individual text elements found (e. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. "Letter Recognition Using Holland-style Adaptive Classifiers". In order to achieve that we extend the existing holistic text recognizer implemented last year and implement a deep text detector, which can filter the image and pass only text bounding boxes to the recognizer. Mozilla is using open source code, algorithms and the TensorFlow machine learning toolkit to build its STT engine. Also do not JSON. Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks. Jasper is an open source platform for developing always-on, voice-controlled applications. We have built a dictionary of millions of different possible entities, which we can rapidly lookup in your text using our matching engine. The uSpeech library provides an interface for voice recognition using the Arduino. See the TensorFlow Module Hub for a searchable listing of pre-trained models. zn, vr, jk, jb, vr, xj, rp, hl, im, fi, an, lz, pu, bk, vt, tr, bu, xp, rn, fs, yz, rz, ho, xq, yf, mn, jo, cw, hg, tk,