Production funding for Lakeland Currents is made possible by Bemidji Regional Airports serving the region with daily flights to Minneapolis-Saint Paul International Airport for information available at BemidjiAirport.org. Closed captioning for Lakeland Currents is sponsored by Niswa Tax Service tax preparation for businesses and individuals online at NiswaTax.com Lakeland Currents, your public affairs program for North Central Minnesota produced by Lakeland Public Television with host Ray Gillnow. Hello again everyone and welcome to Lakeland Currents where tonight we're talking about a topic very near and dear to my heart called Let's Go Fishing. This is not a show about how to catch fish although these people probably could give you some pretty good hints on how to do that. But it's about a program that's statewide that's impacting people in care centers and nursing homes who get an opportunity to go fishing where they just haven't had that for maybe a long time or maybe even never. Everybody's cases is different. But it's a great cause and I'm happy to have my guests here to talk about that tonight. To my immediate right is Phil Nelson. Phil Meyers. I'm sorry Phil. Phil Meyers who is with the Brainerd Chapter and Phil you're also the president of the Brainerd Club. That's correct. And Carol Worley is to his right and to her right is Anita Williams and Anita is from the Grand Rapids Chapter. Welcome to you all. You're all color coordinated so you look pretty good. You look pretty official. Just tell me a little bit about how long you folks have been working with this program before we get into what the program is. Phil how long have you been with Let's Go Fishing? I'm in my seventh year. I started as a volunteer seven years ago. I was volunteering for three years. Got on the board four years ago and served. But on the board since then I've been chapter president for the last two years. And how about you Carol? Six years ago I got to be a volunteer probably two to three months into my volunteering and they asked me to be on the board. So I've been on the board ever since. And I came on board with Let's Go Fishing about four years ago and I was just basically invited to a board meeting first off and they said we know you love fishing and doing this kind of stuff. Hey do you want to join in? So you got hooked? Well who could give us kind of an overall perspective about what Let's Go Fishing is all about. Phil you want to do that? Sure. I think it's very simple. We put smiles on people's faces and fishing is a mechanism we do that. And we're primarily serving seniors who no longer have the means or ability to go out and do this activity on their own anymore. So what do they like to go out and fish? And sometimes they just like to go out in the boat and get in the lake. Many of them have ever given up hope of ever seeing the water again in their lives. And when they get on they tell my friends it's like seeing a six year old at Christmas sometimes. The absolute joy you see in their faces. And when they're done if you don't like getting hugs you wouldn't like this job. We get off the boat and get them to their transportation. There are always two or three hugs. People run up and give you a great big squeeze and walk away from that just feeling phenomenal. And how many trips does a typical chapter take in the summer? In Brainerd we schedule nine trips a week all summer long. Depending upon weather conditions we'll take out 900 to 1100 maybe 1200 guests over the course of a summer. Some of those may be repeats but it's busy. We're blessed to have over 100 volunteers that make this chapter work. It's an all volunteer organization. We have a tremendous board of volunteers and a tremendous base of volunteers that makes it all work. And through all the years at least the Brainerd chapter we have never had to cancel a trip because we didn't have a volunteer to take the trip out. You all use pontoons rather. Do you have one pontoon in Brainerd or do you have more than one? We have one pontoon in Brainerd. So when you say you're going out that many trips the pontoon's really being used a lot. It's getting used twice. Monday to Thursday we have morning and afternoon trips and a Friday morning. So are they like four hour trips? Two and a half hour trips. This year we introduced a couple evening trips and four to six hour trips too so there have been a couple of times we've run three trips a day so the demand's coming up. So you have someone who captains the pontoon? We always have a crew of two. We focus the training on besides rules of the water. You have folks who have some disabilities disadvantages. I would guess you have to have people understand how to deal with that too, don't you? Well, a little bit. Most of the care facilities provide volunteers that come out or staff members that deal with any issues that may be along those lines. Our primary training is operating the boat in safety. It's all about being safe on the water. It's not our guess before they get off the shoreline and onto the boat making sure they're seated and comfortable and just making sure everybody has a good time and staying safe. If there's issues with individuals sure, I mean we some of us just have learned to deal with different things because sometimes you have people that maybe have a little dementia in cases like that and sometimes things can occur and we deal with it but there's all the staff members doing that that we are. Our job is to get them out and you've got to learn how to figure out how many wheelchairs can you get on that pontoon at one time and make that, you know, how does that work and removing seats and So you take the seats off the pontoon when you have the wheelchair if you need that spot for a wheelchair. And then when you have how many can you take on your average pontoons that are used in let's go fishing? We often take 10 10 people? And if you have a wheelchair that might take off an extra seat so perhaps just 9 but because our pontoon is how long? 28 28, it's it's plenty long for your mobility because our main job probably like yours too is putting on worms and casting it out they do all the really men and they do the fun part for sure. Is there a minimum requirement do you have to belong to a care facility or a nursing home? What if it's a person that lives alone? Can they be involved with this? Yes, actually we service our senior center also home instead which are people that just stay at home and then sometimes the caregiver comes in once in a while we have one person who twice I took out in the last month who was blind and his wife came too and she drives them. They don't have to have a bus, a handicap bus or any kind of a bus that's suitable for disabilities to bring them there and Philly we talked a little bit about the software but how many lakes do you actually fish in the Brainerd area? In the Brainerd area we're fishing four. We're fishing Gull Lake, Rice Lake, Bay Lake and Pelican Lake. And how about from your area? We mostly fish pocaigama but we do bring it to Trout Lake when we do a veterans program and then there's another lake but the main lake that we use is pocaigama lake. We keep the pontoon right there on the lake. The other thing I was going to share with you is the mission statement of Let's Go Fishing and it's bringing nature's healing and well-being to seniors, veterans and the disabled. So that kind of sums up what you know our mission is as an organization as a whole. So when you say veterans I would guess that if there's someone who's handicapped they wouldn't necessarily have to meet an age requirement. Would that be fair? Exactly. Is this all catch and release? What can people keep them if they want to? They can keep them if they want. The one service we don't provide is cleaning fish. So if they wish to bring, you know, keep some home and they can keep a legal limit of fish, take it home and clean them up and cook them up themselves. That's just fine. For many of these folks and I know a little bit about the organization, it's the first time they've gotten to do this for a long time, isn't it? Oh, absolutely. And to even catch a perch. Yeah. That's five inches long. That's what we love. Yup, it's fun because it's action and that's pretty exciting to see and the sad part is that sometimes it's the last trip that some of them ever get to make. But one of the things on board that I do often is take photos of the people with the fish and then if they are able to or if the caregivers are able to tell me where their son's telephone number or where I can text it and oftentimes right on board the boat, I send off the picture to great granddaughter or great grandson or something and they text back right away of course and they're really, oh my gosh, grandpa you are out fishing. That is the coolest thing. So it's pretty fun to have the immediate satisfaction for them to even let someone else know here's what I'm doing something I'd love to do. In your area how many volunteers do you usually deal with roughly? Yeah, for volunteers we have about 50. We're always looking for more. So if anyone wants to be, yeah and we took out nearly 1200 participants this year fishing and on cruises 1000 volunteer hours so we're working hard to service the needs of our area. So when you guys take these folks out fishing to the nursing homes or the care centers or wherever, do they make all the arrangements with you beforehand? How does that, how do you line this up? Yeah, they schedule the trips I mean different chapters do this differently we have a big schedule that event at the beginning of the year where all the care facilities come together and it's kind of like a lottery. You know they draw a number one to the 30 or so facilities and then number one you get to pick a spot and then right down to 30 then go backwards again and they can fill in spots so before we even put our boat in the water we have 90% of our trips booked for our care facilities so they all know when they are and they have their sign up sheets to get their people organized we always contact them a couple days before the trip just to remind them go over any last minute details, find out when you're coming any special needs wheelchairs or otherwise so we know what to do and we're ready for that and they show up in the dock with the bus and we get them on board, you have fun. So how many institutions do you actually work with in the Brainard Lakes area? I think it's around 30s Wow, are there some that don't get involved with Let's Go Fishing or are most of them involved? I think most of them are, if we find one that isn't we will contact them and that's right and it's a free program There's no charge for them to come and do it so all our money is raised on the side that supports this program and so by making it free for the seniors I think we get more to participate. So how many chapters of Let's Go Fishing are there? Do you have any idea statewide? There's 24 right now in Minnesota and one in Wisconsin, it's in Rhinelander, Wisconsin. And this was started by Joe Holm, right? Correct and you know what year he started this? I don't know the exact year I'm thinking it's about 12 years ago roughly and it began. You operate a lot and you mentioned this a little bit but you have sponsorships you get local businesses to help sponsor and what does this sponsorship pay for? Well the sponsorship you'll see decals on the side of the boats if you just Google Let's Go Fishing you can go to the wet main website and see pictures of a lot of the boats and you'll see the decals around the side and all those people pay a thousand or fifteen hundred dollars or some fee every year to support that so the local chapter I mean that brings us in maybe twelve thousand dollars a year which helps covers our expensive fuel insurance, equipment maintenance new things like that that we have to do to keep the boats up they're safe. And for the Itasca chapter we do a couple of fundraisers we have a fishing tournament and so we get sponsors for that and then we use their names and the advertising on the back of the t-shirt and we do a lot of different stuff that way and then we also have a fish fry every spring to get everybody really excited about starting out the fishing season and so we get sponsors for that and we serve five hundred, six hundred people fish that can come and buy a fish dinner and it's fun we're all so excited to get together and put on our yellow bright yellow shirts and volunteer and help them.  It's a pretty good fundraiser for us that and the fishing tournament those are too bigger and then we do apply for some different grants and there's different things to do to help raise the money that seems like you know there's always someone to help out and be part of the organization when they hear how many people you service you know from veterans to the elderly to disabled so you've got a chapter in Bemidji I believe is there a chapter in Bemidji? Maybe not. There was. I know Grand Rapids, do you have the chapter in Grand Rapids? Yeah we are a task a chapter and I think St. Cloud, does St. Cloud have a chapter? You know I'd have to look exactly and see where those Yeah I'm just curious Spicer area in London, Alexandria and so do you folks get together on a yearly basis at all your boards and do you talk about the challenges of raising money Yeah we have a yearly and it's some really interesting training they try to give us a lot of support and help us to be able to lead our individual chapters and give us some ideas we can have round table discussions and get to meet great volunteers like this at those meetings too I really enjoy going Yeah they hold those every year in the spring And then if it's rainy or windy you just maybe have to reschedule if you get those this year we've had a lot of that this fall anyway We don't go out if it's stormy or even the summer if it gets too hot it's dangerous as rain you have a bunch of seniors on the boat so it's a call we make usually on the day and that's up to the captain's prerogative to decide if he wants to go or not and contact the facility and they will discuss the options and make a go or no go decision And I know that funding has been an issue off and on as it is with most volunteer organizations it's just impressive about how many people do volunteer the number of volunteers you have in Grand Rapids and Brainerd that's a phenomenal number of people So how many captains do you have trained in these two communities that just operate the boats with the pontoons We must half our volunteers Oh half our really so I know they go through a training program on water safety and all that sort of thing That's pretty impressive That certain protocol and with the liabilities the captains and co-captains need to know just exactly what the product what is the let's go fishing way of doing things you know for example get those life jackets on the participants before you get them on the boat And how long does your season go? Starts in May Our starts in May and goes until I mean it's I think officially considered done and finished now So sort of into September Although one great thing we have in the Atasca chapter is accessibility to a couple of ice houses so we can take out ice fishing even Does Brainerd do that too? We've talked about that but we have not taken that to the next level yet One of our volunteers a couple of them have ice houses and they say hey I love to go fishing I would be more than happy to Wow So you fish mostly pan fish when you go out so you fish along the weed lines I would guess for the most part So you get crappies Do you get some crappies and sunfish? Sunfish and when we get into big rock bass that's when it gets to be fun because when you get bigger fish on they have a ball and you get into a pound or two rock bass and you get a good fight on your hands when they have a lot of fun doing that So do you obviously need rods and reels and you need tackle is that all part of your fundraising efforts then and does that stay with the pontoon when you get that kind of equipment there that kind of thing Yeah Has there been any efforts to get funding from the state at this level for this operation or is it mostly through volunteer fundraising? Most of the chapters are fundraising for themselves The state has embarked on a capital campaign to try to raise some more significant money at the state level to try to expand the program into more areas than that currently exists And we pay a little of this every year that covers our insurance and administrative overhead costs So that's coming out of the money We collect, you know, for our chapter we pay some to them and the rest is going into taking care of our boat and any other equipment that we need So your overhead is really minimal when you don't have any labor costs Well you do wear out a pontoon It's amazing how If you weren't out of pontoon Yeah, we had to get a new pontoon Absolutely We could procure a new motor this fall that will be on the boat next spring You must be hard on pontoons We're taking a lot of runs I'm sure Yeah, we've replaced our carpet with a hard floor much easier to clean We've had re-upholstery done because it was falling apart We want it to be comfortable and the people want to have comfort inside Yeah, have it set up on a nice basis So if someone wants to get involved as a volunteer with Let's Go Fishing from your communities How do they do that? Probably the easiest way is Google Let's Go Fishing and that main page you can go to there's a link to all the different chapters and then there's phone numbers for every chapter that you can call and find out more information Call any of the presidents or anyone on the board or you can even ask any of the other people that are volunteers hey, how can I do this how I got involved is someone that was on the board said hey, you love to fish why don't you come join me at a meeting and see if you're interested and I thought oh, so I think that's important that us as volunteers try to put that invitation out there to others to see hey, maybe this is a good fit for you guys So some of your volunteers must be younger like you are or you have jobs and you're doing things as Phil you're said yourself employed and you kind of work out a schedule with your own business Sure, I can Do you act as a captain from time to time? Yes, absolutely I haven't got out as much in the last two years since I took over as president because I've been busy with a lot of that stuff but fortunately my schedule allows me to take a morning off here but for younger people working a regular full-time job it can be hard because most of our stuff is during the week so we have a lot of older volunteers and as one of our former board board members said we're really old people taking older people fishing How is that though? One thing we've done this year we partnered with a census which is a large company in this city and they have something that they do for their employees which I think is good they want them to volunteer and so they partnered with us so their volunteers actually come and drive our boats during work time they get released from work during the work day and it's not called vacation it's called volunteer You see more and more companies doing community services for their employment, don't you? And these are younger people, these are millennials so we are talking millennials working with an elderly population which is a really good match That's really cool It would be nice to see something like this for kids because one of the things and I've been in the fishing industry a long time we see so many kids who are not interested in the outdoors as much mostly because they don't have an opportunity they don't own a boat and they don't know somebody they can go walk down along their shore and go fishing and it's a similar thing with your clients when they were sitting in nursing homes before they didn't have these opportunities it's pretty cool We do take some youth groups out from time to time I've had a group of somewhat challenged youth where our focus has always been seniors but the boats here will accommodate anybody if you have a group and you think it would be good for them by all means contact us and we'll do what we can to get you on the water We volunteer with camp confidence also about wounded veterans we take out actually entire families if we know there's a veteran that would like to go out fishing we will find out to their family five generations on the boat really? it's just wonderful we'll take out entire groups of families I've done some work with wounded veterans how do you make the connection there? that connection is usually made through the legion they are the ones that do the contacting and they contact us and we work it out I bet they really appreciate that too everybody does it moves us too it's very emotional I don't think and we've got some video we're going to show when we're doing this interview and I hope people can see the smiles on folks' faces I haven't seen your video yet but I'm sure that will come through won't it? when the boat starts leaving the dock and everybody is saying goodbye you'll see the smiles because you think of some of these folks have been in some of these facilities for a long time most of the time if their families can't come and take them out they're pretty confined aren't they? and you know what's one thing that really surprised me is taking some of these from the nursing home out and they actually have kind of lost the ability to communicate they can't communicate like you and I can but they catch a fish and you see that excitement and you know that they are very aware that they did something really good they're the big cheese for that moment and that freedom and that sense of self-confidence that you can see you can't sit and have a conversation maybe with some of them like we are able to but it's amazing how they you get such positive feedback from them and you can tell that they just are thoroughly enjoying fishing or being out for a boat ride do the facilities make the decision about whether the person is capable of I would guess you have to because you're not in a position to know that if they can get them there along that same line I had an Alzheimer's patient out an old gentleman I've known for years he had no idea who I was anymore but as soon as I put that rod reel in his hand that was all there and he was catching fish like crazy really? and then when I met his couple of his kids later in the year and we talked about told him what a great time his dad had out there I mean they were so thankful because they had seen dad and dad talked about fishing but they don't know if he was talking about fishing when he was a kid or something just happened now so what we did was good for dad and it made the kids feel real good because I knew he was out still having a good time wow that's really cool I've taken a 93 year old woman out fishing to and part of me sometimes they say I hope someone's taking me fishing when I'm 93 that's pretty cool so if someone wanted to make a contribution to the organizations in Grand Rapids how did they do that? they would contact any one of us board members or Bev Taylor is our president contact her you could also go through the website let's go fishing I task a chapter make the contact there that's what I would say or you can go contact the whole organization corporate office and then make your mark money for a certain chapter too if you want to or you can give just to the organization as a whole so there's it's easy to give sure and how about the brain organization where do they go if they wanted to make a contribution of some sort really same thing look on the website I guess you can call our number 218-454-FISH that's our chapter phone line so 218-454-FISH you might get a recording right now but somebody will be happy to get back to you and that would be similar same information if someone wanted to be a sponsor sure and what does a sponsor get out of it you said to put a sticker on the on the pontoon do they have any other benefits there's stickers on the pontoon there's also a portion on the website under each chapter that lists all the sponsors that are there we try to promote those sponsors when we have some of our training facilities our end of season we try to encourage people to do business with those who sponsor us and we do that too at our Christmas party or annual Christmas party we put together a really beautiful Christmas party for all the volunteers and we do give plaques and awards to those that have sponsored and been donors so they can hang that in their business wall that hey we are a proud sponsor of let's go fishing and what have you got in store what's coming down the pike anything new next year I'll put you all in the spot you mentioned a partnership with children and that may be an interesting thing to look into to have future partnerships with kinship for example I think that's a great idea there's so many young kids that would get excited a lot of us old timers had grandpas and grandmas that took us fishing that's missing for a lot of families and a lot of kids now so that would be something really wonderful to do for several years in a row we've taken fifth graders from forest lake elementary and green rapids and they can sign up whoever and we will rent and bring a couple more pontoon so we can get everybody in in a two day period of time and let them have a little fishing trip so it can be the first introduction to some of those youth that may not otherwise have a chance to be on the boat or go fishing or enjoy that well we're out of time thank you all for coming it's a wonderful organization and I know you guys are doing great work thank you for the volunteer efforts that you put in it's just a marvelous program thank you for being on the show you've been watching Lakeland Currents where we're talking about what you're talking about I'm Ray Gildow so long until next time


Right. Welcome back from the coffee break. I hope you all got what you wanted. Right, the next presentation that's coming up is already mentioned above here. It is the next generation of transcripts. As you know, we have enabled transcriptions and annotations of historical documents for quite a while. And this is also the reason why we now have to renew what we're doing. We have to work on transcripts. And we have users worldwide with millions of already-processed pages. And of course, we want to keep this up, but we also want to do it in a better way. So now, we want to make transcribers ready for the future. We want to make it more powerful, and we want to make it more user-friendly. And this is why I invite to the stage the people responsible for this. And each data is already here. And the others will join later in the presentation, as I've heard, Florian Stauder, Florian Krull, Felix Dietrich, Philipp Kale, Fabian Hollaus, Sebastian Coluto, and the world is yours. The microphone is on. Great. Yeah, this is really one of the highlights of the conference here. We would like to talk a little bit about where we are heading next, or what we are up to next, and what we are thinking about. So I would just like to introduce my colleagues here, really, from the mainly marketing and development team, so rather product management and development. And I would just like to highlight a little bit the most important areas, the key areas that we will be focusing on in the next couple of months and years. The first area here is that there will be more tech, or that we will think about tech, technology more. For example, we are currently re-evaluating the core components of the tools that we are bringing to you. So the technological basis, we will take a web-only trajectory. So we're trying to simplify the whole technological foundation of what we are doing, because maintaining two tools is a lot more work than maintaining only one. And we will try to bring the best possible user phase to you that we can. We will include new, transformer-based recognition technology. So we've heard already a little something about transformers. So yeah, they will bring a new level, especially to out-of-the-box performance. So how much do I have to invest before I can recognize the text? How much training do I have to do? Yeah, then we are trying to bring technological improvements to large-scale applications, especially with our processing API and with the on-prem that I already alluded to earlier. We will have improved trainable layout recognition. This is very near and dear to our hearts, so really to enable you to identify the different parts of text and to basically contextualize them and to identify them as what they are in terms of structure. So that's it for technology, mainly, or what is technology proper, you could say. Then the second very important item here is more social elements. So we would like to bring the community closer together and interface it and network it even more than we already have. One element here will be user profiles, so that you can present what am I working on, what are my interests, and that other people can see. OK, there's someone working on Church Slavonic of the 12th century. Great, there are only five people in the world that do this. I'm one of them. I didn't know there was one right next door. Yeah, we will improve and include more collaboration features so that crowdsourcing projects and citizen science projects will be easier to handle and more fun to do as well for the participants. Transcribus Learn will be a very interesting feature, so you will be able to train the users in paleography with Transcribus. We will hear about this too. OK, now also in the social category, we will have an open beta program where you will be able to try out features before they go live and to tell us what you think about them. Maybe they're rubbish, maybe they're excellent. And last but not least, we will focus more on the topics of content and search, these are really the very big strategic goals that we are following because content and search and also going beyond that, intelligent technologies that might accomplish just what we heard about earlier in the panel discussion to make AI a part of the conversation. I mean, it's not going to be the fancy historical chat but in the next one or two years probably that will replace historians. We're light years away from that. Maybe it's not even happening anytime in the future but we will try to make everything more intelligent and to bring the content and what's really interesting in the documents more to the users and this includes researchers and the general public equally. One first small step here is read and search as a CMS so you will be able to set up your own read and search as opposed to having to go to one of our developers and having them set it up for you and federated search will be a very big topic too. So bringing read and search instances together and being able to search across several read and search websites. So yeah, I'll hand over now to the team and next up is Florian or Flo as most of you know him and he will be talking about the web and what we have in mind for it. Thank you very much. So a warm welcome also from my side. I'm the guy that is borrowing you all day with emails and so I'm employed with marketing but also with product management and we'll talk a little bit about what is coming up with the move towards the web. You might be familiar with this cute little guy, right? It's called the Wolpertinger. Every time you launch your expert client you will see it on your screen. But there is some things going on. We have a second tool you can use to use transcribers which is called TranscribersLight and it's based on the web. And here you see what happened over the last few years. So it's only two years since we have TranscribersLight now but there is only even 55% of all users are already using TranscribersLight. So there's definitely a tendency that goes towards the web and that's also why we are focusing more on the web. It's just more convenient. It's just you launch your browser, you can go into TranscribersLight and then you have the features there. So we basically went from the left side, from the expert client to the right side, to TranscribersLight, over the last two years we have both of them now. You might ask, is this it? Some will scream you out, please keep the expert client. I can feel you. I can really feel you. It is a tool that has been proven to work very well over the last few years. So it has been developed now. We have heard almost 10 years old now and TranscribersLight is only two years now. So it's quite young. But what we want to do is to bring both worlds together. So having two of those tools is kind of expensive in terms of maintenance, in terms of time, you need to spend a lot of resources on both of the tools but they are basically doing the same, right? You can do your text transcription. We're Transcribers and a lot of other things. But what we are trying to do is to bring together the best of two worlds. So in the Transcribers Expert client, there are many, many features. There's like a saying in the team, nobody knows them all. So there's so many features that not even our proficient experts in the team know all of the features. It's quite robust. It's tried and trusted. So a lot of users, especially the power users, those users that process really large amounts of pages use the Expert client. And it has a nice little 90s feeling when you have all those little buttons in that gray interface. But on the other hand, we have TranscribersLight. So it is rather quick. You can quickly access it via your browser. And we hope that it's more intuitive but we're not there yet. We know that. And it's also platform-independent, which is really important because just having the Expert client to work on kind of a lot of different systems is a little bit tricky, as you might tell and as you might have encountered some Java problems during your work with the Transcribers Expert client. And it's responsive. You can theoretically also work on your smartphone. So our plan is to have just one Transcribers. It's called Transcribers and that's what it's about. And we are moving towards that. It will be in the web, certainly. We are trying to make it simple and powerful. So combining the really strong power of the Expert client with the simpleness of TranscribersLight. That's what we are trying and focusing on during the last weeks and the next months to come where we will be working on this. We will focus to build a really easy user interface but try to bring as many features as possible at the right time. So you don't need like, I don't know, 70, 80 buttons at the same time when you really just want to do one thing. So that's certainly one of the focuses here and we want to empathize the important things. For instance, the models. Like the models are kind of hidden. Of course you'll find them also in the Expert client but we will give them more space so you can also share your models that you can have your unknown page for your models. You have them for the public models now but it's more important to really showcase all your work because in those models there's a lot of work going in. We want to try to make workflows as fluent as possible so we try to focus really on workflows. Where does a user start? How do they work? What is the next step? They are probably trying to take. But we won't forget the little thing so maybe a little thing that is coming up, bookmarking. So you can bookmark your documents or your pages because when you leave your browser it would be nice to just start at the same time or you just want to remember a page. But there's some more concrete things to come and I will hand over to my colleague Flo and now we got a couple of those. Thanks Flo. Yeah, I just want to give you some quick insights into the web development team what we're working on so Johannes Kleine and Andrea Haider. And... So first of all, we're working on Transcubus Learn. So on the left side you see how it should look like a little mock-up. We have an image snippet with the text, with the word we're trying to learn or trying to guess underlined and the context around this word where you can then check the word and see a little progress. So that's really, really helpful for courses which want to learn reading the old handwritings and we try to implement some of these features like setting and editing tasks for example for professors in which they can exactly choose the words which are kind of really tricky or hard to read or just randomly select them. And also of course the owner of the task should be able to read the statistics so which words were guessed or read correctly from the group and of course they will be able to establish and create learned groups so everybody in the learned group can take part in these created tasks. As Andi mentioned before, we're also working on a content management system for read and search that will be fully integrated in Transcriber's Light so it's not decoupled but it's just integrated so you can just look at your material in Transcriber's Light and see how it will look like as a read and search instance so you can edit the transcription at the project description or the citation and just make it available for everybody without any login or Transcriber's account. So you also will have all the powerful search features such as tag search or full text search in this read and search instance. And as we heard before, Transcriber's is your social technological tool so we try to get you more involved into the development and we try to use our Transcriber's beta instance so that we can release new features as early as possible and have you and anybody who wants to participate in testing and giving us feedback and how to improve certain features and how you use these features in your process and in your work. So we will try to release new features weekly and then have more stable releases which will come out probably every two or three months and this is because we want to have you as a community to give us more insight in what features are really important to you and which should be in Transcriber's. And with this I hand over to my colleague Philip Kalle who will talk about processing API. Thanks Florian. As you said, I want to talk about some news from the API apartment today. So some of you might already know that the Transcriber's platform exposes in the HTTP API since the beginning. It's essentially what our user interfaces, the expert client and light use to accomplish things on our servers. It provides all the means to import data, accessing collections, managing documents in them, updating transcriptions, starting the various tools and finally exporting the data. But now consider this use case, you have your own editing pipeline set up in your systems and want to employ our handwriting recognition in the process. With this old classic API you would need to handle all the different requests to our servers from import to organizing the data, starting the tools and finally exporting the format you need. All of this is a complex protocol of requests that needs to be implemented in your tool and we often saw in the past that this becomes very cumbersome and requires a lot of effort to get right. So one example where this came up in the recent past was the Enrich Europeana Plus project where the development was also co-funded where it was about to integrate our handwriting recognition in the transcriber phone platform. So we thought about those pain points we had identified and tried to address them in the development of this Metagrapho API. First, we tried to boil down all the requests required for this to a minimum. A simple image in and text and layout out workflow. No document collection management is needed for this and as the data does not have to be served to user interfaces we also could get rid of the persistent storage of image data. As a starting point for developers we added documentation according to open API specification and a swagger UI that lets you try out all the requests in your browser. So no one has to figure out how all the bits and pieces come together by themselves. Nevertheless, you have all your custom trained models available using this API and also the vast number of public models from our website is accessible. To give you an idea of the workflow with this new API let's go quickly over it. After authenticating to our account service all the data for processing one image file is submitted in a single request. The image can be either encoded into the request data or if your images are already online you can pass in a URL and our systems will fetch it from there. Optionally you can also add layout information like regions or regions and lines if you have your own analysis tool set up. This also allows to select a region of interest that has to be processed. Finally you need some configuration to tell our systems what to do with the image. This is specifically a text recognition model ID and optionally some parameters to tweak the layout detection. The response will contain a process ID which can be used to pull the status of the processing just as you know it from the old API with the jobs and the final response contains the detected layout elements and the recognized text or in case you passed in layout information the missing elements will be enriched in there. The standard output is now simplified JSON format so you don't have to fiddle with the details of the page XML format. In case your tools need this format we have endpoints available for fetching also page XML and I2XML. In the backend all this is accomplished with an enhanced transcriber's job system meaning you can submit a sequence of images and they will be processed in parallel as processing slots are available. We also offer reserved resources for this something we call Fastlane meaning you have a specific amount of slots reserved for your user account and your processing also takes place at times where our systems are under high load. To give you an idea of what this API can do in terms of throughput we measured the system with the validation set of our German current writing model. Actually we did a lot of tests but I took this here for an example and we came down to a processing time of 15 seconds in average per image for this data set. From this tests we derived an average throughput per day using three reserved processing slots of five to 10,000 images per day depending on your material. What's up next for this API? We will add support for applying your own custom trained layout line detection and layout detection layout analysis models in the near future and we are proud that this is already used in various applications besides our own transcribers AI tool where you can try out HDR in your browser. It is already used in the DocWis platform by content conversion specialists and Gobi by Entranda, both of which are some of our early adopters and I want to thank them for their valuable input at this point. Also coming up next is the European Transcriber Fund platform that will transcribers handwriting recognition using this API soon and more to come hopefully soon. Thank you very much. And now my colleague, Sebastian Coluto will take over and give you some ideas what the on-premises solution means. It's working. Okay, so hi to all. Still have already mentioned my name is Sebastian Coluto and I'm one of the senior software engineers here at the transcribers team. So I've been basically part of the team since the beginning of the first project which was in 2013. Yeah, in the next few minutes, I'm going to present you our current and future development of the on-premise solutions for transcribers. So first of all, I want to make it clear what on-premise actually means. So basically it's just the installation of the read co-op or transcribers software on the local machines of a client so as the name suggests on their premises. And this can be needed due to several reasons. First of all, and this is maybe the most common one, in our context it becomes necessary when the data that is the document images in our case cannot leave the customer premises due to data privacy issues. For example, when there's sensitive information like patient data involved. Another reason on the other hand may be that you would want to make use of your own hardware or the hardware of the customers like a computer cluster. And using several on-premise worker machines you could thus relieve the central transcribers workers from doing all the heavy lifting during the training and the recognition itself. So basically I've divided my talk into three parts. First of all, I want to introduce you to the existing on-premise solution which we have developed for our customer. And secondly, I'm going to give you a broad overview of the architecture we have in mind for not only our next gen on-premise solution but also the next gen of the transcribers backend in general. That is a microservice architecture. And in the last part of my talk, I will outline our concrete plans for the implementation of this architecture both in the near future and in the long run. So our first setup of an on-premise solution was done last year in 2021 for the state archive of the canton of Zurich. And the goal here was the setup of an HDR system that recognizes some thousands of images that could not leave the house due to privacy issues as well, where images containing a sensitive medical diagnosis from patients. We basically settled for an easy solution, a simpler solution. We got SSH access to one of the local machines which was basically just a regular mid-range desktop PC with a consumer graphic card that had six gigabytes of virtual memory and was Q.10 compatible. Also it has the Ubuntu 20 operating system installed. And the software we set up on the machine was the baseline detection basically using the default model as well as the HDR recognition and training both based on the PyLayer engine. And all of the software could be accessed using Linux share scripts. And the files to be processed had to be copied to the local file system where they could be accessed and processed by our scripts. And it was also possible to use one of our pre-trained public models which in this case was just copied to the local machine. And in addition, the state archive of Zurich were using the expert client in its local mode to create ground truth data locally for training their own models. However, the user interface could not be used to start the actual training or recognition processes themselves. Those had to be started using the console scripts instead. So although the simple approach is very straightforward and has its advantages, there are some obvious problems to it which we are currently working on. So first of all, and this is more of a problem on our side basically, there was no page counting mechanism or any other sort of restriction on the processing integrated. So it was only possible to offer kind of a flat rate or fair use solution to a customer based pretty much on a mutual agreement that the software would not be used outside of the specific project it was purchased for. So to overcome those problems, we will work towards a solution where our customers need to digitally sign both the hardware that is specifically the GPUs using the unique universal identifiers and also the models to be used with a private key at our site and which then can in turn be unlocked using a public key on their premises. And this way we can ensure a bit more that the software is only used for the purposes of the contract. But apart from those issues on a contractual level more, so to say, the main technical problem in our regard is the lack of so-called containerization. So this means that the on-premise solutions in the current state have to be installed in every machine separately, including all its dependencies and also requiring a specific operating system. Also apart from the local mode of the expert client, there are no UIs like Transkivo Slider or Read and Search available in the on-premise solution and everything has to be run using console scripts which in turn requires someone from the technical staff more or less to start all the processes. So how to overcome those problems? And the answer is, and many of you may have already heard of that, is microservices. So this is a really hot topic currently in computer science in general and it's basically depicted here on the right-hand side. So this will be the future architecture not only for the on-premise solution but also for the Transkivo's backend in general. It is based on isolated microservices, each of which can then be developed and deployed individually and which are communicating with each other. And the opposite of such a microservice architecture is called a monolith which is here depicted on the left-hand side. It's basically just one single database interacting with one business logic which then in turn interacts with one or even more user interfaces. But this architecture leads to the problem that first of all changes to the monolithic platform become increasingly complex as the platform grows and thus the management overhead is growing over the years. In turn, the microservices shall be designed to be small and isolated keeping them at a low management cost for the team that is working on that service. But on the other hand, which can also be seen on this picture, there's also a downside to this approach which is really the increase in communication between the different isolated services which obviously also gets larger as the system grows. However, this architecture in our opinion still has the advantage when it comes to deploying on-premise solutions especially as it enables us to only deploy certain services on the local machines of a customer and not being forced to install a whole platform which is infeasible in most of the situations. So next, I want to give you a short overview of the technologies that are usually involved when dealing with microservices. So first of all, the central unit of all of the microservice architectures is called the container. So a container is essentially just a unit of software that contains all of the necessary code and its software dependencies. And for that purpose, it utilizes the virtualization feature of the host operating system which in its core, it's just a functionality of an operating system kernel to divide its resources into multiple user-defined spaces. And the most popular software for containerization currently around is certainly Docker. And I would even argue that it's one of the reasons why microservices have become so popular in recent years. But there are also alternative solutions like LXD containers or Portman.  So the most straightforward way and default way to do this would be to create a web service with an API for each of the containers which are then communicating with each other. But as this direct form of communication can become quite error-prone for growing system also, there exist more advanced solutions like setting up a centralized message broker or decentralized service mesh that handles this crucial part in the architecture. And then finally, the last part in a microservice architecture would be that once you have decided about the containers and the communication between them, you would usually use an existing software solution for the orchestration, so-called, of your microservices on your specific hardware. And here popular solutions include Kubernetes, which is an open source software written in Go by Google, or the OpenShift project maintained by Red Hat. So lastly, I'm going to talk a little bit about the rough roadmap for our further development of the on-premise software. So in a way, you could argue that our current on-premise solution is already kind of a microserved architecture, microservice architecture, because each tool, like the HDR training or recognition, is installed as separate tools and can be accessed with a command line interface which serves as the input and output API. So here the task in the second phase here depicted would be on our part to really clearly define the input and output of each service. And then maybe create a web service interface for them to also handle the input and output from image services, like Trip.IF, and then to create Docker images for them or using another software like LXC to encapsulate each service and make it easily installable on the premises of a customer. And then all services could be, as it is now, accessed by a command line interface, or maybe also an adapted version of the expert client that could start the training in the recognition jobs. However, in this stage, it may not be feasible to integrate all the job management and monitoring systems that are available in the user interface currently. So as a rough timeline for this phase, we have seen we are forcing the end of the year or the beginning of next year as the deadline for a functional solution, also due to the fact that there are also already some interested customers we are dealing with. So the interesting phase is then the third phase, which is the broader outlook to the on-premise implementation. And this is tightly connected to our plans for the next generation of the transcripts back-end in general. So as already mentioned earlier, the plan here would be to develop a system where each of the individual services could be installed individually on the premises of different customers and being able to communicate with each other. So I think one of the first steps for the on-premise solution here may be to integrate our current web-based user interfaces into the system that is transcribous light or maybe already the next-gen version of it. And afterwards, we have to settle... Afterwards, after we have settled for the core technologies we want to use, the task we will be to containerize each of the different services that make up the transcribous platform now, which is authentication, splitting up our current database into probably several database services, a job management system, e.g. based on a message queue technology, but also the very important service for delivering and storing the files. So currently, we have custom software developed for that, but in the future, we may support any sort of triple IF-based image hosting services. But also additional services like the full-text index, which is obviously very important for read and search, will need to be containerized. So the goal would then really be to incrementally add those services and functionalities to the on-premise solution and our next-gen transcribous backend as well in order to minimize the need for re-engineering. So this third phase is obviously a bit more vague. And thus, we cannot promise any concrete timeframe for the implementation. Also because it is so tightly connected with the rethinking of our backend, which is a very large task, obviously. So I strongly believe, however, that we are able to implement and showcase the first demonstrator, including read and search and transcribous slide, maybe by the end of next year. So maybe at the next year's transcribous user conference. So with that said, thanks for your attention and I will hand over to Felix Dietrich, who is showcasing the next gen of the recognition engines. Okay, hello everyone. Yes, my name is Felix. I've also been with the development team for quite a few years now, actually. But one or two years ago, I switched positions in the team and my current job is to make sure that we do not lose track of current developments in the field of AI and more particular deep learning. And yeah, the thing is keeping track of research can actually be quite difficult because as we already heard in the keynote earlier, it can sometimes be decades between the discovery of a new architecture and the time when this new architecture actually becomes practical. So in that regard, I have a slightly more technical presentation for you. Don't worry, I won't throw any equations around, but I do have some numbers for you. And yeah, these are basically the number of papers published every month containing a certain keyword in the title. And as you can see here, we have, for example, CNN stands for convolutional neural networks. That's the thing that completely revolutionized image processing, and it basically made a lot of stuff easily possible that was impossible to do just 10 years ago. But the architecture itself is, as we already heard, at least half a century old, but only in late 2012, 2013, we can see that this actually started to take off. And today we have like almost one and a half percent of all computer science papers published actually mention CNNs in the title. Another big architecture is the recurrent neural network, or RNN for short, and in particular the LSTM, which is a certain variant of it. And yes, this has also been around for a long time, I think, 80s, 90s, something like that. And just like the CNN, it's only gained traction in the past few years. However, there are actually quite a few problems with RNNs, we will talk a little bit about them in a moment, and you can see they never quite took off as much as the CNNs did. And finally, with the benefit of hindsight, we can actually say it was not a bad idea of us to start investing into transformers, because you can see in the last two years, this architecture has completely taken off. And even more interestingly, unlike the other methods, the transformer architecture is actually completely new. This didn't exist before around 2017, and the half a percent line there you can see is basically papers talking about some other kind of transformers all the time. And then once the first paper about transformer architectures was published, this has completely gone beyond everything. And today, more or less, every big new development in the field of AI almost certainly has some kind of transformer behind it. Now, well, we already heard that transformers have once completely taken over the field of natural language processing. Here's just another short overview of all the things that have come up in the past few years. We have seen very, very general character, word prediction models. We have seen translation models. We have seen models that can summarize things very well. And yeah, the transformers basically enabled a huge amount of stuff that just was not possible even, I don't know, three or four years ago. And on the other hand, even more recently, the transformers also overtaken the image processing world. Here we can see the top models on the ImageNet competition. For those of you who don't know, ImageNet is basically a huge collection of hand-labeled images. I think it's a couple millions of images labeled into thousands of classes. And yes, we can see that the beginning in 2013, the first practical convolution networks have seen a lot of progress. But in the last one or two years, all the top models are either directly transformers or at least somehow based on transformers. And yes, for our sort of core technology, which is the handwritten text recognition, this is really important. So here's just a short overview of how this is currently happening with the existing models, for example, with PyLayer. So you basically feed in an image of text. You have this convolutional network that basically sweeps over it. You then feed these images into the recurrent networks to get a sort of language, a long-term sequence understanding, and then eventually you get sort of character probabilities out of that. The big issue here is that recurrent networks are very limited in the size that you can train them because they are not easily parallelizable. So the next iteration of a recurrent net always depends on the previous one. So if you want to compute a whole sequence, you can only do it iteratively. And this, as I said, drastically limits their usefulness and it's part of the reason why they never really took off. Although people have tried for years and years to make recurrent networks work. They came up with all sorts of tricks and eventually someone discovered one of these tricks is actually all you need. You don't need the recurrent network at all. And this is now the sort of simplified transformer architecture. So what happens in this new process is we basically take a very powerful vision model and a very powerful language model and we just feed in an image in certain patches. So this is a key difference. Before images could sort of be arbitrarily sized and now we work with images of fixed size and we split them up into patches and then we can generate a sequence and then we feed that into a vision model. And the great thing about this architecture is not just that it's much faster, much more easily parallelizable. So you can train much, much larger models. The second benefit is that you can actually train these vision and language models independently. So you could sort of take out this vision construct and for example train it on the ImageNet dataset just by refocusing the output layer on the image classes, spend I don't know a huge amount of resources creating a very good image recognition model and you can do the same with the language model, for example by training it to predict next characters or words in a huge text dataset like Wikipedia and then you can go along and just take these two very powerful models and for the handwritten text recognition engine the only thing we really have to retrain is this layer in between the two models. Yes, so just a little summary of how this works. It is a much simpler architecture. It is much more efficient. It allows us to train much larger models which can also learn a lot more stuff. And another key new feature compared to the old models or the ones that we are currently using is we are sort of getting rid of these predefined character sets that you have to come up every time and we sort of use a general purpose set of word tokens. I don't want to get into it too much in detail but let's just say it contains at least for European language almost every kind of special character and combination that you would need. So with these new models, we will actually no longer have to deal with these model dependent character sets. And if you ever tried to use a pileer model trained in one thing, a base model and trained on a new data set, you probably know that this is not very cool. Yes. Okay, I'm happy to announce that yes, the first of these transformer handwritten texture recognition models is actually already available in the platform. We've been working on it for over a year now and just recently started rolling it out to users. So I guess over time, I hope everyone will get a chance to try it out and our immediate goal for this first iteration of models was just to have one really huge general purpose model that was trained on all sorts of languages and writing styles and also on handwritten and printed text. And so far it contains at least six languages with English, French and German being the most dominant ones but we also have a couple other ones in there. And yes, this kind of model is ideal for just trying out the recognition. For example, if you have a completely new collection of documents, you don't have any sort of expert pre-trained models, you can just see what kind of recognition can you maybe expect from that model. And yes, on the other hand, you could also perfectly use this to create new ground truth because when you don't have an expert model, that means you probably also never have created any real ground truth. So you can go ahead and use this model, create a lot of machine-generated ground truth that you then only have to correct so you no longer have to do everything by yourself. And yes, this was only just a very first small taste of what's about to come. We are constantly working on this and for example, just recently we purchased some new hardware that will allow us to have much faster recognition speeds using these transformer models. And in the near future, we hopefully also will be able to allow users to at least fine-tune these models. So the basic training, this is very resource-intensive. This takes weeks, even on our highest end machines, but fine-tuning on a specific small cases of documents that can actually be done in a reasonable time. And that is something that we hopefully will be able to allow in the future. And another thing that will happen definitely in the future is we will add more and more training data to this model. Hope it can learn to generalize to even more languages. And we are also always working on improving the accuracy. So right now, the model is in direct comparison, at least as good as PyLayer with a specifically trained language model and it easily beats PyLayer alone. And in the future, we hope that with further improvements to the architecture of the transformer, we hope to go even beyond that. And we are also always trying of course to improve the pre-processing and things like the layered recognition which also really become important at this level because we have sort of reached almost the top of what's possible. If you think back to the ImageNet competition data set I showed you, the increase in the transformers over the convolutional nets, it was definitely there, but it's not much. So we are actually probably already dealing very close to the areas of what is even theoretically possible. And yeah, with that, I would like to give the word to Fabian who will now talk a little bit more about our new forms of layered analysis. Yeah, hello, my name is Fabian Hollos. I'm rather new to the team. Working on computer vision and for the last months, I've worked on a new trainable layout analysis tool for recognizing forms or for doing layout analysis and table recognition. So this new layout analysis will pretty soon come to transceivos and contrary to that transformer thing that Felix talked about, this is not for general purpose. It's really designed to be trained on just a limited set of training data. So let's say 10 pages with layered analysis, layered annotations, and then to train this on this small ground truth set for your own documents. It's based on an open source framework which is called Detectron 2. So this was released at the end of 2019. And the cool thing about this is that it allows to use different architectures, but also they have released cool models, which can be easily, so you can, these models were trained on a large dataset on natural images and you can easily find unit on document images, for example. So definitely advantages, I would say. And one cool thing about this framework allows for different kind of segmentations. So here we use segmentation for the layout analysis and two of these segmentation types are, one is semantic segmentation and the second one is instant segmentation and I will just briefly introduce what this means. So here you can see an example of semantic segmentation. So there we have an input image. Anyway, you have an input image and each pixel of this input image gets assigned and is labeled then as belonging to a certain class. For instance, we have here classes like persons or beach. Yeah, so this is this, but one thing is that if you have neighboring pixels that are belonging to the same class, like you say, you have persons which are directly neighboring and you cannot differentiate between the two objects which are belonging to the same class. So the current layout analysis tool in Transcubus, P2Pala, it uses this semantic segmentation. Yeah, and contrary to this is instant segmentation which the new layout analysis makes heavily use of where you just care about certain instances of classes. So in this case here, we have just one class or two class. So the first one is the background and the second one are here, the persons. And the cool thing is now that you can differentiate between two objects even if they belong to the same class and also if they are neighboring. Yeah, and this is supported by this open source framework that I mentioned, the Tectron tool. And yeah, so we are now using it for layout analysis. And so here you can see an example of the architecture. So it's based on a classical CNN architecture. But still it works pretty well, I would say. Yeah, so basically it all depends on bounding boxes. So in the first bounding boxes are assigned to certain candidates of objects and then these bounding boxes are further refined to segmentation masks. So here you can now see an example of the output of the layout analysis. So in the middle here, these white fields, they are anonymized. And but here we have an odd document which is written in a tabular form. And we trained it on, I'm not sure, I think it was about 10 annotations. So this worked pretty well. So here you can see yellow just means regular words. Red means here, detail signs. And blue are digits. And as you can see, the framework is capable of segmenting these different kind of classes even if they are directly neighboring. Like here in the upper right, as you can see here, these two yellow bounding boxes, which are directly below, they are still segmentated into two different objects. So that's a nice thing. And what you can also see here, so this green grid is the table that is found by the new layout analysis. And I will talk about this in the next few slides. So again, this table recognition is based on instant segmentation. And the cool thing for this is that you do not need any visual separators like in the regular table, you have, let's say, a vertical line and a horizontal line. But if you do not have this, like in this example here, again, this is anonymized. Then of course, it's more difficult to differentiate between the different columns and the different rows. And what we are now doing is we apply this instant segmentation. We train two different models. One is for rows, and you can see here an output for this. So every color marks a different instance. And the other one is for columns. So this is here. So, yeah. As you can see, maybe here, the third row from the bottom, this contains basically two text lines, but still it can be found. Yeah. Now we can use this information to some very basic image processing to find the intersection between the rows and the columns. And I will show you just some example, how this works. So, here we have two tables. And here you can see the output of the table recognition.  And here it's worth mentioning that only the vertical separators that are found here are the ones that were used for training, so the output is correct. And of course, this gives us a certain flexibility because we can say, yeah, we only want this column and neglect the other ones. So that's a nice thing, I would say. So in this case, I think we was decided that, let's say the third column or something like this is the name, and so we just want these certain type of columns. Here is a further example. So now here we have vertical separators, but we also decided to split the virtual separators. So you can see here in the large column there are first names and last names. And we also trained the model to separate between these two names, and although the separation is not really visible, or yeah, it can be grouped, of course, but there is no vertical separator. So the model is capable of finding this vertical separator. And for the last example here, we have here some vertical separators, but no horizontal separators. And yeah, but we trained the model on these. So of course here the extraction of the rows is more difficult, and here you can see the result. So this works pretty good, I would say. So at the current stage, we are just using straight lines for separating between the rows and the columns. But yeah, so at least in the examples that I showed you, this is sufficient, I would say. So also in this example here. Yeah, it's just for a short summary on the layout analysis, it's based on instant segmentation, and compared to the previous layout analysis, the output is more precisely, I would say, and especially in the case if you have very limited training data, so which can be attributed to the fact that we have this detection tool framework, you have a very good, you have the pre-trained models, and you can, which are pretty good, I would say, and you just have to fine tune them on your own data set. So you fine tune it on your own collections or your own documents, and for the table recognition, you also just provide an annotated table, and so we then make use of the page XML in order to train a row model and a model for columns, and this is then done in the table recognition part. And yeah, so this will then come soon to transcribals and sit. Right, thank you very much for this insightful and, well, quite techie presentation. We now have a little bit more time for two or three questions, so if there are any, it would be cool if Sarah could hand you the microphone. No questions? Everybody waiting for the lunch? All right. They're blown away. Pardon? They're just blown away. Okay, that's what Andy says. Right, okay, if there are no questions, then we've got one. Do you want the microphone or don't you? I hand this question over to the guys who do the development. What do you say? Go ahead. No, we've got a microphone here. Yeah, I'm not sure I understood the question correctly. Do you mean an indexing function for creating word lists, basically, indexes? Okay, so you mean lists of the stuff that you tagged in the text, right? Okay, yeah, this is definitely valuable input, so we have no concrete plans to do this yet, but I think we'll make an out of it and see what we can do because if there's a concrete need for it, then we are definitely happy to, yeah, think about it. Okay, Anu Mika says it's already possible in the expert or, yeah, yeah, for the tags, but not for, yeah, not for anywhere. You mean only for tag material or for any kind of word that you have not tagged, for example, you might want to have a list of, or you want, yeah, yeah, yeah, with tags, yeah, that's true. It is solved, yeah, in read and search, for example. You can also display the tag lists there. And in transcripts light, is it? It's not in there yet, okay. So is it limited to just read and search because I'm not sure? Okay, yeah, okay. Yeah, and it depends on the kind of tag that you're using, so it's not there for structured tags yet. Okay, are there any other questions? Dave? Anymore? Okay, then we have a question there in the back. I'm coming to you. Would it be possible to use the API to score a single page ground truth against a load of models, which is a question for the tech team and a question for you specifically is if I run a single, if that's possible, and say I'm trying to score against 40 different models, what would be the implication for credits? Yeah, I mean, how do you mean score? You would like to? I'd like to, what we often do is we have a new text and we see if it works in any of the models that we have or in any of the public models. But if we can do it through the API, obviously it's a lot quicker. So you're thinking about having a test run with your models. Okay, I'm not really sure. What does the tech team say? Philip? This is something which at the moment needs to be done manually, I think, if I understood your question correctly. So you would have to try out the different models you think are good. We internally talked about this feature actually a lot, but at the moment it would require a lot of processing resources to really test all the different models against one page. So it's useful to really make a selection of models and try them out. Of course, this is something we have on our list to always think about when it comes to overhauling things. Yeah. Well, now you want to know how well a custom trained model works because a custom trained model can do better, especially in terms of time performance because you want to be quick about recognition too. So the transformers have not solved that problem yet. And coming back to the second question, what that means in terms of credit consumption, that would depend a little bit on what the feature really looks like. If it's really just about scoring, then I imagine we can implement a system that would be cheaper in terms of the per page price because you're basically interested in will I be processing a lot of pages with this model? That's what it boils down to. And yeah, I think we would be willing to facilitate that. What you can use here, which is free of charge, is the sample document functionality. So you can create samples from your data with where lines are picked randomly. And then you can run the processing on that as many times as you want without paying credits. Okay, I think we're in a bit of a hurry, aren't we? We've got a new question here in the second row. Okay, let's do one more very quick question, please. One of the speakers told about TRHTR, so the transformer models. And one of the slides said it's now available in transcripts to try. Where can I find it? Okay, so yeah, you need to be on the list. Short answer. I think we really need to get lunch going now, okay? I think we have to move on. Yeah, thank you.