How Can I Integrate Electronic signature in Android

An android digital signature is a secure electronic signature created on Android devices using specialized software. It ensures the authenticity and integrity of documents, making it ideal for businesses looking to streamline their signing processes.

airSlate SignNow provides a user-friendly platform that allows users to create and manage android digital signatures seamlessly. With its mobile app, you can easily sign documents on your Android device anytime and anywhere.

Yes, airSlate SignNow offers several pricing plans tailored to meet various business needs. Each plan includes the capability to utilize android digital signatures, ensuring you can choose the option that fits your budget.

airSlate SignNow includes features such as document templates, real-time tracking, and a secure cloud storage for documents. These features enhance the efficiency and security of using android digital signatures.

Yes, android digital signatures executed through airSlate SignNow are legally binding under various electronic signature laws, such as the ESIGN Act and UETA in the United States. This provides peace of mind for businesses needing to ensure compliance.

Absolutely! airSlate SignNow offers integrations with various business applications, allowing you to enhance your document signing process. This means you can use android digital signatures within your existing workflow seamlessly.

Utilizing android digital signatures with airSlate SignNow increases efficiency, reduces paper usage, and accelerates the signing process. It empowers businesses to work smarter, enhancing productivity and customer satisfaction.

You can use android custom gesture as signature in android app development. You can save the gesture signature to an image file also. Below article show you examples.Android Capture Gesture Signature To Image Example

If I'm understanding your question, Python is running on a server, and the app has to talk to the server.Typically, Android apps will communicate with a server through HTTP.  The backend service will have a RESTful interface which will return JSON more often than XML, since JSON is much lighter weight for parsing.Since you're talking about a chat app, the push side -- receiving the message -- is a little trickier.  You will probably have some Comet-type long polling code on the app to handle that.I don't really use Python, I'm more of a Java guy, so I had to dig around a little.  Apparently Python has something called Web Service Gateway Interface (WSGI) for running Python apps in web servers.  So your Python code will likely be talking to an app server through WSGI.The best way to think about the interface is if you had to do this chat app in a browser.  And you had to download one HTML page with one JavaScript file to run the entire chat app.  How would your JavaScript work with the server to operate your chat?  The Android app will work about the same way.If there are specifics about the Python code, like it's already written for browser chat or something, please add the details to your question and I can improve my answer.

Almost all the payment gateway in India has the SDK for Android/Iphone. CCAVENUE too must have the same. But best I have experienced for mobile app’s in terms of smooth operation & auto OTP read functionality : PAYU.I am sharing my experiences with different payment gateways in long run & I really hope it shall be helpful for you, in making the right decision:CCAVENUE: Good one when comes to Technology. But when comes to customer support, it sucks!EBS: Good one when it comes to onboarding. But sucks in customers support & further requirements. Its slow in terms of processing things for a high growth industry.Instamojo: My worst experience so far. Though it was in 2016 2nd quarter. I have heard they are doing good now. But still my experience has not been good. Onboarding: I will rate 2 out of 5. Post Onboarding Support: 2.5 out of 5. Settlement of Funds: 2 out of 5. Customer Support in terms of on boarded merchants: 0 out of 5My Bad experience was that they will change the rates anytime they want to. They didnt keep the same rate as its listed on the website. At the time of sales pitch I was given 1 rate. And when onboarding was almost done, the rates where changed heavily. Post onboarding Settlement was like a nightmare. And then Settlement was on hold again for further rate increase. So in my experience of 3 months I had 3 rates and the point of contact was not a very appreciable person. More over when the employee who studied from IIT and when handling your account, if talks out not right & dont make good sense of things, its like a worst experience.RAZORPAY: Its the OK Experience. Onboarding is quick. Then Technology is Good. Problem is the business team, which does not respond faster. Earlier I had a 5*** experience in terms of communication with them. Seems this is the problem with every company which in start has good communication and once they have good number of merchants onboard, they slowly start avoiding. I was being put on communication through one of the founders to a business team person. In 2 months the person was barely available for 2 min with a promise of call next time. I will say, its experience killer. For me RazorPay was awesome in all aspect. Now customer First is loosing. The Cost is standard. The settlement is bit longer. But overall will rate 2.5 out of 5. ( Earlier I had rated it 4 out of 5 as per experience )PAYU: 4.9 out of 5. BUT there is a problem: You will have to ensure all the documentations are good. The onboarding process is bit time taking. But later I realised that this problem is the best problem. Because when you are getting on a ride, better to learn/get knowledge of everything before getting into the ride. Its like getting trained with stuffs for a good go.Only 1 Problem which I have seen is: In my so long experience with them, many a times my SPOC was changed and it was quite frustrating when you have to deal again with a new person. But I will say, I get settled with the new person every time because I know I will have to follow the process. It may take some time. but on sidelines, your operations are never taken for a game as like Instamojo or EBS.Trak N pay: I have recently came across this PG & its support system. Awesome guys and quick response. Best part is they have awesome innovations going on, and as a customer of a online website/marketplace etc.. you will find the awesome ready to use solutions from them. But they do have one problem.. They are not advertising much about their service. Might be because they are not funded startup. But apart of this problem I will give 4.9 to them out of 5!Its my experience with these guys. I have experience with couple of others too, some are less known and another was CITRUS. CITRUS is a part of PAYU now, so my review of PAYU applies for the same.IN terms of Not exactly Payment Gateways but their side product is Payment gateway, my experiences:Paytm: Never Got a Revert from customer care. Its may be because they are on high growth and miss/forget/overlook small merchants like us.Mobikwik: They are good in terms of discussion. But further business development people were not quite understanding and always will talk about the rack stuffs, which actually was not my need.AirPay: Never got their Support on call.PAYPAL: My Longest marriage with this company always have cultivated a strong belief: Customer is god! And the one who is making the payment is everything, no body else matters. So always focus the best on customer support & make sure your Paying customers never go away/ and never get disappointed in any way. ( I have never used Paypal domestically. Its for my international business where payments have to come as Inward Remittance. We have Paypal Account since 2005–6 ).Hope this will give you some idea about making the right choice.Cheers! And Best of Luck.

There are different kinds of machine learning (ML) algorithms, some can be small enough to run on-device while others can be too large for a mobile device.So the answer is, it depends solely on the problem you are trying to solve and what level of accuracy is needed.Normally there is a tradeoff, if you want the model to run on-device then you must sacrifice some accuracy.Thus you need to find a sweet spot between accuracy and model complexity (run-time and memory footprints).There are so many optimization tricks that can be used to make ML models run on device.One simple approach is to keep computations simple, like avoid floating point operation especially calculations with very small numbers. You can truncate small numbers to zero and probably lose just a bit of performance in terms of accuracy while gaining some speed in terms of computational time.And if you can, you should binarize the calculations. Like use hamming distance as the similarity measure where possible.You can also prune the models by removing less useful parameters (same as zeroing out small parameter values) or sub-modules.You can also use a simpler child model that is trained to mimic the larger, more complex, parent model.Also some models are small enough to fit on a mobile device, like:A simple linear regression model can be coded up from ground-up using native code such as C/C++ and deployed on-device.The same applies to logistic regression models, linear support vector machines (SVM) and basic neural networks (NN).Even deep learning (DL) models can run on some mobile devices. With TensorFlow for example some models can be deployed on device.On Android there is the neural network application programming interface (API[1]), a quote from the site:The Android Neural Networks API (NNAPI) is an Android C API designed for running computationally intensive operations for machine learning on mobile devices. NNAPI is designed to provide a base layer of functionality for higher-level machine learning frameworks (such as TensorFlow Lite, Caffe2, or others) that build and train neural networks. The API is available on all devices running Android 8.1 (API level 27) or higher.Other models can be too large to fit on a device, like the large scale convolutional neural networks (CNN). This is where you can use tricks like [math]1×1[/math] convolutions to reduce model complexity and hopefully fit the model on a mobile device.Building on-device artificial intelligence (AI) solutions is very tricky especially for applications like image retrieval which require access to powerful online servers with large memory to hold information about millions of items.If however your interest is building solutions that are somewhat limited like style transfer, face recognition and object detection (limited to a few objects) then it is possible to build such models on-device.Thus you need to be specific with what you are actually trying to solve because there is no general approach but rather many problem specific approaches one can use to squeeze AI models on a compute/memory restricted mobile/embedded device.Hope this helps.Footnotes[1] Neural Networks API

If you have an additional product, MATLAB Builder JA for Java, you can produce a .jar file from your MATLAB code.However, the .jar file requires the MATLAB Compiler Runtime (a freely redistributable component that you get with MATLAB Compiler and MATLAB Builder products) to be present. The MCR has a much larger footprint than is suitable for the typical Android device (it's like a copy of MATLAB itself, without the user interface).You could think about eitherRunning your MATLAB .jar file remotely on a server, and having your Android application connect to it, orInstead of using MATLAB Compiler and Builder products, use MATLAB Coder, which will convert a subset of the MATLAB language directly into C code. This C code doesn't require the MCR, and could be compiled to run directly on Android. Make sure your MATLAB algorithm falls within, or can be expressed in, the appropriate subset of the MATLAB language.

Write an odata service or a web service in abap backend system..call it from the native Android app or write a Fiori mobile application.