Mathieu Blondel

In my last post, I was writing about this impressive Chinese character recognition demo using AJAX on the client side and Support Vector Machines (SVM) on the server side, for the recognition process. Well, I don’t know if it’s just a coincidence (this demo was from 2 years ago) but Taku Kudo released last week the backend he’s using as free software. Needless to say that this was awesome news for me! I know the basic principle of SVM but time to learn more about it I guess…

His project, called Zinnia, has been rewritten from scratch to be more flexible and reusable. Models for Japanese and Chinese are included but models for other languages can be built easily provided that you have training data. I’m pretty sure that this package could also be useful for Gesture Recognition because it’s so close to Handwriting Recognition…

For the sake of comparison, I wanted to evaluate how Zinnia performs compared to both Tomoe and my own HMM experiment. I used the same evaluation corpus as I wrote about in earlier posts, that is two sets of 50 kanjis written by a Japanese friend of mine and me. The characters have the correct stroke order and were drawn carefully. Therefore, the results below indicate how the different recognizers perform in ideal conditions and don’t indicate how robust they would be in more difficult conditions.

Tomoe - Zinnia

1st match accuracy is the percentage of characters that were recognized as first match.
5 matches accuracy is the percentage of characters that were recognized in the first 5 matches.

You can download my evaluation script for Zinnia here. Tomoe’s evaluation script is sitting in Tomoe’ SVN, in the benchmark/ folder.

A few remarks:
- Zinnia is notably better than Tomoe in terms of accuracy
- Zinnia is about 7 times faster than Tomoe, making it a good candidate for an embedded platform
- In both cases, 5 matches and 10 matches accuracy are about the same, meaning that it would be enough for the user interface to display the first 5 matches only.

Project Tegaki - Zinnia

Due to lack of training data, my personal HMM experiment (project Tegaki) was only conducted over a set of 50 characters. However, Zinnia supports over 3000 characters. For fair comparison, I thus created new models for Zinnia using the same training data as I used for my experiment.

Zinnia was trained with only one sample per character, using the same data as Tomoe, which is template-based. While SVM seems to be able to cope with only one sample per character, it’s a little bit more complicated to do that with HMM because of the need to find the parameters of the Observation Probability Density Function (e.g. mean and variance for a Gaussian).

A few remarks:
- My experiment is slow, which is probably due to the fact that I’m using Character-level models. Stroke-level models are known to scale much better.
- My experiment has slightly worse accuracy, which is probably because I’m only using two features per observation.

Handwriting database

If you follow my adventures in the world of handwritten Chinese character recognition, you probably know that I’m planning to create a handwriting database website. This database will aim to 1) make it easy and attractive for people to contribute their handwriting samples and 2) make it easy for the database staff to manage and organize what is supposed to become a large collection of handwriting samples.

The database will use a client/server architecture. So far I’m thinking of four important clients:
- A client that people will be able to use directly in their web browser, using my web canvas
- A client for the Maemo platform
- A client for the Iphone
- A multi-platform client for the Destkop

A client of slightly lesser priority would be a Facebook application.

The handwriting samples collected will be distributed in free software license. For projects like Zinnia or Project Tegaki, this will mean more training data and more means to evaluate the performance. I consider this database one of my priorities among my free software projects but it’s going to be quite hard for me to find time for that before December…

Contribute

As always, more people are welcome to contribute.

To download the source code of my work,

$ git clone http://www.mblondel.org/code/hwr.git

web interface

I came across this impressive page (in Japanese), which shows a demonstration of Handwritten Chinese Character Recognition using AJAX for the user interface and Support Vector Machines for the training algorithm.

Looking at the Javascript code, I was surprised to see that, unlike my web canvas, it doesn’t use the <canvas> tag! It simply uses a combination of Javascript and CSS. Even though it has a few quirks, the interface is quite responsive.

The recognition process itself happens on the server side but thanks to the use of AJAX, the results are displayed very smoothly, without the need to refresh the page.

Taku Kudo, the author, explains in the page that he’s using the handwriting data from Tomoe. However since Tomoe uses a template-based algorithm, it only has one handwriting sample per character. I’m impressed that Taku Kudo can train his system only with one sample per character. Overall, the accuracy is not very impressive but I think it could improve a lot with more training samples. That’s why my handwriting database project is going to be very useful. I’ve been willing to try SVM in addition to HMMs so the fact that this project uses SVM confirmed my interest for it.

Taku Kudo’s page has neat stuff regarding Natural Language Processing and Machine Learning. He published a lot of libraries as free software, including Mecab and TinySVM. If you like fancy stuff in AJAX, you’ll also like his Japanese Input Method.

If you haven’t seen all the cool stuff that Johnny Chung Lee is doing with a Wiimote, you should definitely take a look at his projects. You’ll see that Johnny is not only a good engineer but he’s also very good at making his projects attractive.

He has a video on how to make a cheap interactive whiteboard or how to turn your laptop into a tablet pc, with a Wiimote. This could be pretty handy for using Chinese character recognition!

In my last post, I was calling for contributors to write a web canvas using the <canvas> tag. If you don’t know it, <canvas> is a new tag specified in HTML5 which allows you to draw using a Javascript API. It is already supported in Firefox, Opera, Safari and is supported in Internet Explorer through a third-party Javascript.

Since nobody responded to my call (sic), I decided to tackle it by myself. It turns out that it was a nice little project. The canvas Javascript API is very similar to the cairo API so it was easy to use. I also improved my level in Javascript a lot. So far the web canvas supports draw, import (JSON), export (XML), save as an image and replay (stroke by stroke animation).

You can try it by using the online DEMO.

What can it be useful for?

- I’m planning to use it for the handwriting database website that I wrote about some time ago. While it will be possible to contribute your handwriting using a pygtk client (Desktop or Maemo), you will also be able to contribute your handwriting using your browser directly. Not having to install any program should help increase the number of people contributing their handwriting.

- A second way of using it would be to do handwriting recognition directly in the browser. For example, one could install Tomoe (or my recognizer when it’s ready ;-)) on the server side and the web canvas on the client side. Since Tomoe has Python and Ruby bindings, this is fairly easy!

You can reuse the web canvas for your own projects if you like but I would appreciate if you could send me any feature improvement. In particular, the web canvas has a bug under Internet Explorer that I couldn’t figure out…

Source code (GPL) : gitweb

Canvas

If you didn’t read my previous post, for short, project Tegaki is a framework for handwritten Chinese character recognition (HCCR) written in Python. It includes reusable components and is a placeholder for experimentation. The goal is to create the next-generation open-source HCCR software but it may be useful for academic researchers as well.

One reusable component is the Canvas. This is the user interface component that allows to draw characters. In addition, the Canvas supports “replaying” the character (stroke by stroke animation) and setting a background model (to help users draw an unknown character). It is multi-platform.

Example of a character drawn using the Canvas provided by libtegaki-gtk

The Canvas has a get_writing() method. It allows to retrieve the Writing object for the handwriting currently displayed in the Canvas.

XML representation

The Writing object supports reading from and writing to an XML file. The XML file can optionally be compressed using gzip or bz2. On my hard drive, I have a small set of handwriting samples. 500 characters take about 10 MB. That’s why compression is very useful.

The XML representation of a handwriting sample looks like that.

<character>
  <utf8>無</utf8>
  <strokes>
    <stroke>
      <point x="306" y="163" timestamp="0" />
      <point x="303" y="163" timestamp="21" />
      <point x="303" y="166" timestamp="29" />
      [...]
    </stroke>
    <stroke>
      <point x="266" y="240" timestamp="912" />
      <point x="270" y="240" timestamp="917" />
      <point x="273" y="240" timestamp="925" />
      [...]
    </stroke>
    [...]
  </strokes>
</character>

Renderers

I’ve recently added support for what I named “renderers”. They take a Writing object as parameter and generate a visual representation of it. Since I used the cairo graphics library as drawing backend, the representation can be saved to PNG, SVG and PDF! Those renderers will be very useful for the handwriting database website that I wrote about in my previous post!

Complete character renderer

Stroke order renderer

Stroke order with each single stroke

Stroke order with stroke groups

Strokes can be grouped together when the stroke order is obvious. However, this requires to know which strokes to combine together. A dictionary must be created for that. A entry example would be:

駅 1,1,3,1,4,2,2

<canvas> HTML tag

The canvas I was writing about above is written in pygtk and is intended to be used for the Desktop or for Maemo. However, in the case of the handwriting database website, since we want as many people to contribute their handwriting as possible, it would be nice to not require any particular installation. For that, a canvas directly in the browser would be the ideal solution.

One solution would be to use Flash but I would prefer to use the <canvas> tag. It can be used in combination with Javascript to do drawing in the browser. It is supported natively by Firefox, Opera and Safari. It is supported in Internet Explorer through a third-party Javascript called ExplorerCanvas.

I am looking for a contributor to create a new canvas using this technology. The canvas should support drawing, displaying existing handwriting and replay (stroke by stroke animation).

For more information:

• Canvas (HTML element) (Wikipedia)
• Canvas tutorial (developer.mozilla.org)
• Canvas painter (a paint-like application in the browser)
• ExplorerCanvas (by Google)

GIF stroke animation

Even though GIF uses a patented compression, GIF is still the only format with support for animations and wide support in the browsers. Therefore it would be very cool to be able to generate GIF stroke animations from a writing object.

I had a look at python-imagemagick and Python Imaging Library (PIL) but they both seem to have very limited support for GIF animations. So I’m thinking of writing my own library for GIF generation in Python. Byzanz, a software to create screencasts as GIF animations, can be used as inspiration because it includes a GIF encoder. It also supports color quantization (using octrees) and dithering. From what I see, it should take less than 1000 lines of Python code.

I read a little bit about color quantization. I found it very interesting. Here’s a short explanation about color quantization for those who don’t know about it. Basically, each pixel in an image may have three components Red Blue Green. For a 400×400 picture, this is about 400*400*3=480KB. To gain space, an idea is to store colors in a palette (a table index => color). Then each pixel only needs to refer to the index in the palette instead of having to define the three components. For a 256-color palette, this saves two bytes for each pixel. However, since we now use 256 colors only instead of 256 * 256 * 256 = 16,777,216 colors, there’s a color precision loss. The challenge is thus to find what colors to put in the palette to have the smallest precision loss possible. For example, we may want to put in the palette colors that are the closest to the most frequently used colors. This is a 3-dimensional clustering problem, thus it reminded me of Machine Learning, a topic in which I’ve been very interested recently.

For more information, I recommend the reading of those Wikipedia articles:

• Palette
• Indexed color
• Color quantization
• Dithering
• Octree

Codename Project Tegaki

I wrote in a previous post about my first experiment with applying a modern technique, namely Hidden Markov Models, for handwritten Chinese character recognition. I’m quite motivated in making this more than just a single isolated experiment so I decided to give a name to the project. I named it Project Tegaki. This is going to be the codename for the effort starting from now. Tegaki means Handwriting in Japanese.

Project statement

The aim of Project Tegaki is to push forward the creation of the next-generation open-source handwritten Chinese character recognition (HCCR) software.

Currently, the only open-source package for HCCR is Tomoe. This is a project that I have been contributing to and that I used for my Google Summer of Code project, “Japanese/Chinese handwriting recognition on maemo”. Maemo is the open-source platform used by Nokia PDAs. I have decided to start Project Tegaki as an external effort because I considered that Tomoe would not be a good environment to welcome the effort. However, if the Tomoe community is ready to help me in this effort, I will be happy to merge Project Tegaki back into Tomoe once Project Tegaki becomes ready for prime-time.

Handwritten Chinese character recognition in a PDA…

Here are some goals for the project:

- Free and open-source. The goal is to produce the next-generation free and open-source HCCR software.

- Modern. The software should use modern approaches to Handwriting recognition and be in tight connection with research.

- Embedded. The project must be designed to work with devices with restricted resources such as cell phones or PDAs.

- Online, as opposed to offline. In online recognition, characters are drawn using a device, typically a mouse, a tablet or a PDA stylus. In this setting, characters can be represented as sequences of points. In offline recognition, characters are scanned a posteriori. In this setting, characters are represented as images (width * height pixels).

- Isolated Chinese character recognition. Here Chinese character doesn’t restrict to Chinese language, since Japanese kanji are also Chinese characters! Even though the package should theoretically be generalizable to any kind of character, Chinese characters have some specific challenges and some approaches that give good results for Chinese characters may not give good results with other kinds of characters, due to the unique properties of Chinese characters. “Isolated character recognition” means that user will have to draw one character at a time in a separate box, as opposed to continuous handwriting recognition. This makes things much easier and in the case of Chinese characters, this is a reasonable limitation.

- Stroke order dependent and independent. Both situations have useful applications so Project Tegaki should ideally support both.

Python?

Usually I’m more of a Ruby fan but the project was started in Python due to dependencies on third-party libraries that only exist in Python. Even though I’m slowly getting away from those dependencies, I don’t want to re-implement everything just for the sake of using Ruby. So I keep up with Python.

As it was emphasized, this project is highly experimental. Moreover, a collaborative website will be created (see below) and it will reuse number of existing components. It thus makes sense to use a high-level language to focus on the experiments and to create the website.

Subprojects

Project Tegaki is now split into several subprojects.

libtegaki

This Python library contains functionality that will be useful to other subprojects. This includes array manipulation, character input/output, viterbi decoder…

libtegaki-gtk

This Python library contains user interface elements that will be useful to other subprojects. So far it only includes a Canvas, which can be used to draw characters. It is replacement for TomoeCanvas with some additional benefits:

- Truly reusable. TomoeCanvas assumes that a recognizer is connected to the canvas. However, there are situations when a recognizer is not needed.

- Resizable. TomoeCanvas cannot be resized at will.

- Animation. A stroke animation of a character can be displayed.

- Background character. A background character can be set as a model and animations will be displayed to help draw the same character stroke by stroke.

- Features other than (x,y) coordinates are supported such as pen pressure and pen inclination when available, stroke duration, point timestamp.

libtegaki-gtk is written in pygtk and depends on libtegaki.

tegaki-db

The most successful handwriting recognition systems nowadays use a “learn by example” philosophy. For each character supported, several samples of the handwritten character must be provided to the system in order to learn from them. Because those samples are used to train the system, they are called “training samples”. The challenge for the final recognizer is to be able to recognize unseen handwritten instances of the same characters. This is the ability of the recognizer to “generalize” the acquired knowledge.

A “training corpus” is a set of training samples. A good corpus should contain dozens of handwritten samples for each character. The corpus should be representative enough of all handwriting styles. Collecting all the handwriting samples and designing a good corpus is a huge task for Chinese characters because there exist thousands of them!

Such handwritten Chinese character databases do exist but they have a fee and they are usually restricted to academic research. They are by no means suitable for free software. The goal of the tegaki-db subproject is to create a collaborative web platform to collect handwriting samples. Native speakers and learners alike will be able to log in and contribute their own handwriting. The collected data will be published in a free license so that it can benefit to academic research as well. The tegaki-db will use a client / server architecture.

tegaki-db-client

tegaki-db-client is a client for people to input their handwriting. It will be written in Python and use the canvas provided by libtegaki-gtk. The client will communicate with the server through web services. The client should be distributed for several platforms such as Linux, Windows and Maemo to increase the number of potential contributors. A detailed specification of tegaki-db and tegaki-db-client will be provided later in a separate post.

tegaki-models

tegaki-models is by no means an end-user package and will only be used by developers. It is the placeholder for experimentation. Thanks to this package, model ideas will be tested and evaluated.

I continued to work on new model ideas… However, because my current training corpus is so small, it’s kind of irrelevant to spend to much time on models. The top priority now is to create tegaki-db.

tegaki-decoder

tegaki-decoder is going to be a high-performance decoder (recognizer). It should be a fast implementation of the Viterbi decoder. It will be written in C and designed to work with embedded systems. This is going to be the end-product that people will use. Once sufficient data have been collected, good models have been generated and the tegaki decoder is ready, then Project Tegaki will be ready for real use! Currently, implementing tegaki-decoder is not the top priority.

Roadmap

- Launch tegaki-db and tegaki-db-client.
- Hope that the collaborative effort is successfull and collect lots of handwriting samples from many different people.
- Create new models, especially stroke-based models.
- Implement tegaki-decoder.

If I continue to be the only one interested in this project, at this rate it will take from several months to a couple of years to achieve everything. That’s why I hope I can attract a few contributors.

Download

The work completed so far is still very experimental and thus targets potential contributors. If you want to test it with your own handwriting anyway, please see my previous post.

To download the source code, you can use

$ git clone http://www.mblondel.org/code/hwr.git

or

$ git pull

from the repository folder if you already have the repository on your computer.

The code can be browsed online using gitweb. By clicking the “snapshot” links you can get a complete copy of the source code at a given revision.

See my memo on git if you don’t know it yet.

I published my work under GPL license.

Today I’m publishing the initial results of my experiments on online handwriting recognition of Chinese characters, using Hidden Markov Models (HMM). You can see my post on Tomoe Evaluation for some background.

Download

$ git clone http://www.mblondel.org/code/hwr.git

The code can be browsed online using gitweb.

See my memo on git if you don’t know it yet. I published my work under GPL license.

Requirements

- Python (2.4)
- GHMM (SVN)
- Tomoe (SVN)
- Tomoe-GTK (SVN)

The Python bindings for the last three are also needed.

Folder structure

- data/ contains the raw training and evaluation data
- lib/ contains reusable components
- models/ contains model experiments
- tests/ contains test cases
- character-editor is the graphical interface to edit character data
- model-manager controls the training workflow, evaluation and the test pad

Each model must have an intelligible name. Each model must defined a file called model.py containing a class called Model. This class defines the behavior of the model. A model can inherit from other models in order to reuse common components. My first model is called “basic” so its file is models/basic/model.py.

First model

Here is some information regarding my first model.

- HMM unit: whole character
- Feature vectors: (deltax, deltay) with deltax = abs(x_t - x_t-1) and deltay = abs(y_t - y_t-1)
- Number of states: 3 * number of strokes
- Initial state transitions: 0.5 to stay in the same state, 0.5 to jump to the next state
- Initial state alignment: feature vectors are segmented uniformly and segments are associated with their corresponding state
- Training: Baum-Welch

If you don’t understand anything of the above, you should read more about HMMs ;) I may write an introduction on this journal if I have some time.

Training workflow

model-manger’s usage is as follows:
./model-manager model-name command

My first model is named “basic” so you may replace model-name by “basic”. Possible commands include:

- fextract, for the feature vectors extraction
- init, for the model initialization
- train, for the training
- eval, for the evaluation
- pad, to test the model with your own handwriting

“all” is a command equivalent to fextract, init, train and eval.

Testing with your own handwriting

First of all you should generate the HMMs with the following command:
./model-manager basic all

The process takes less than one minute on my computer. You may see a few warnings because of some issues in ghmm and tomoe. If all goes well, you should see the accuracy of the model.

From this point, normally, you could test the HMMs with your own handwriting with the following command:

./model-manager basic pad

However, for strange reasons, ghmm behaves incorrectly when the pygtk module is loaded. So the above command works but the character results will be incorrect. I need to contact the pygtk or ghmm mailing-list about this obscure issue. For now, you can use the following command:

./model-manager pad | ./model-manager basic eval -s

The results are displayed on the console. The system supports the following 50 kanji only.

一 二 三 泣 漢 温 使 便 旅 族
水 氷 撃 女 安 北 化 忘 妄 近
集 育 坊 訪 防 妨 駅 福 副 神
版 坂 板 金 全 錬 練 業 習 央
決 代 反 想 歯 象 始 初 発 感

Pick a few of them and try them with your own handwriting ;-)! By the way, all training and evaluation data were written by mouse.

Evaluation

match1: 80.0%
match5: 96.0%
match10: 98.0%

始	1	始, 福, 駅, 錬, 漢
旅	1	旅, 族, 駅, 練, 副
妨	1	妨, 練, 錬, 板, 発
防	1	防, 訪, 旅, 族, 板
泣	1	泣, 温, 福, 練, 駅
副	1	副, 訪, 福, 撃, 初
福	1	福, 練, 錬, 副, 駅
坂	3	板, 駅, 坂, 族, 錬
代	1	代, 板, 漢, 使, 駅
反	1	反, 福, 副, 忘, 妄
撃	3	駅, 錬, 撃, 漢, 副
業	1	業, 練, 錬, 集, 駅
氷	2	駅, 氷, 水, 妨, 版
温	1	温, 福, 駅, 錬, 想
育	1	育, 練, 副, 駅, 福
神	2	練, 神, 福, 錬, 撃
近	1	近, 駅, 練, 漢, 福
化	1	化, 練, 駅, 便, 習
一	X
央	1	央, 決, 業, 駅, 発
族	1	族, 練, 旅, 錬, 副
安	4	妄, 駅, 福, 安, 族
象	1	象, 駅, 錬, 練, 集
歯	1	歯, 練, 錬, 駅, 副
錬	1	錬, 練, 集, 駅, 福
習	1	習, 錬, 福, 駅, 漢
使	1	使, 便, 漢, 錬, 練
訪	1	訪, 駅, 錬, 副, 板
漢	1	漢, 錬, 駅, 練, 業
全	1	全, 金, 集, 錬, 福
集	1	集, 練, 業, 錬, 福
版	1	版, 板, 錬, 駅, 集
水	2	氷, 水, 旅, 駅, 便
板	1	板, 族, 坂, 福, 駅
妄	1	妄, 駅, 福, 忘, 練
初	1	初, 駅, 旅, 練, 坂
想	1	想, 駅, 副, 錬, 集
発	1	発, 練, 福, 駅, 漢
練	1	練, 錬, 福, 駅, 板
北	1	北, 坂, 副, 駅, 板
決	1	決, 漢, 便, 練, 坂
坊	X
駅	1	駅, 錬, 練, 族, 福
金	1	金, 発, 練, 錬, 駅
女	5	駅, 妨, 妄, 板, 女
忘	1	忘, 族, 副, 福, 駅
二	1	二, 三, 忘, 歯, 習
感	1	感, 福, 駅, 族, 練
便	3	練, 駅, 便, 錬, 福
三	1	三, 忘, 副, 版, 訪

The results are very promising and outperform Tomoe’s current recognizer. Incidentally, I used the same evaluation corpus for Tomoe and for my experiment. However, a few things must be emphasized:

- My experiment only supports 50 kanji while Tomoe supports thousands of them.
- The evaluation of my experiment is performed using kanji from the same people who wrote the kanji used for training. However, the kanji instances for training and evaluation are not the same.
- It’s pretty sure that using the whole character HMM symbol will not perform well in terms of computation time with thousands of kanji. Usually, stroke or sub-stroke models are preferred.

Interestingly, my recognizer doesn’t do a good job at recognizing the simplest characters: 一 二 三.

Both Tomoe and my recognizer are sensitive to stroke order. However, as it seems, my recognizer is not so sensitive to stroke number. For example, く in 女 is one stroke but it’s acceptable to write it in two strokes. However, if you write く after 一 and ノ, it doesn’t work.

Call to online handwriting database

If you’re a researcher in handwriting recognition and read this, I’m looking for a handwriting database of Chinese characters (kanji or hanzi). Please contact me if you can help me.

What’s next?

- Try more sophisticated feature vectors
- Try more sophisticated initial state alignment
- Try stroke and sub-stroke HMMs
- Collect more data
- Try techniques other than HMMs

In last December, I started a one-year internship at Asahi Kasei, in their Atsugi-based speech recognition group. Even if I have been doing quite a deal of software development, I have been able to study Hidden Markov Models (HMM) and statistics. It turns out that, hehe, I like it!

One year ago, I started to contribute to Tomoe, as part of my participation to the Google Summer of Code. This experience raised my interest in handwriting recognition, especially of Chinese characters. When I studied the Hidden Markov Models, I always kept in mind Handwriting Recognition. “How would I do this? How would I do that?”. This helped me raise more questions and have a better understanding.

One thing that I learned during this internship is the notion of corpus (plural: corpora), more precisely training and evaluation corpus. Three months ago I started my experiment project with Chinese character handwriting recognition. The first thing I had to do was to create corpora. I reused the canvas provided in Tomoe to create a character editor. The user draws the character and it is saved to a file in XML format.

Together with a Japanese friend, we selected 50 kanji. Some simple, some complex. Some completely different, some very similar to others. We each wrote 5 instances of each kanji. 8 instances were intended for training corpus. The data are used to train the system how to recognize kanji. 2 instances were intended for evaluation. The data are used to estimate how good the system performs. The performance is describe in terms of accuracy or error rate. The evaluation allows to measure improvements when one recognizer is tuned or to compare how well two recognizers perform, provided that the evaluation corpus is well designed (large and representative enough).

Well, Tomoe doesn’t use statistical learning yet so I didn’t use the training corpus for it. However, the next thing I did after collecting data was to use the evaluation corpus in order to evaluate Tomoe’s performance. At the time of the Google Summer of Code, I didn’t have this idea, although it now seems obvious to me. Verdict:

1st match: 61.0%
5 firsts: 74.0%
10 firsts: 74.0%

This means that 61% of characters are recognized as fist match and 74% are recognized in the first 5 or 10 results. Considering the first 10 matches, which is acceptable, the error rate is still 26%, which is pretty high. Here’s a more detailed view of the results. Interestingly, we can see that kanji with the same radical or shape are often in the candidate list.

駅      X
妨      1       妨, 姨, 姙, 枋, 枕
坊      1       坊, 垓, 坑, 拡, 択
発      X       癸, 廢
歯      X
全      1       全, 舎, 舍, 早, 果
金      X       昂, 氤, 釘, 覇
板      X       被
忘      1       忘, 忌, 志, 芯, 忠
女      1       女, 冊, 木, 仄, 攵
族      X       楾
始      1       始, 姶, 恰, 娯, 娃
錬      1       錬, 顕, 鍜, 鰊
集      1       集, 賃, 寔, 夐, 募
旅      X
坂      4       扼, 拔, 城, 坂, 披
訪      X       詫, 誇, 就, 駱
水      3       氷, 丞, 水, 妃, 羽
三      1       三, 工, 弖, 王, 玉
想      X       慧, 慂
神      1       神, 裡, 祝, 殉, 術
副      1       副, 歇, 飮, 尠, 飩
安      1       安, 宋, 宏, 免, 案
泣      1       泣, 注, 浜, 淳, 泡
二      1       二, 井, 云, 元, こ
感      1       感
代      1       代, 伐, 陀, 弛, 池
撃      1       撃, 磬
温      1       温, 溜, 塩, 溘, 溝
漢      1       漢, 灘, 嘱
一      1       一, 廾, 弋, 十, 七
象      X       豫
育      1       育, 昌, 匿, 高, 香
氷      2       妁, 氷, 承, 冰, 灰
反      1       反, 皮, 尻, 阪, 伎
業      1       業, 箕, 篇, 賓, 霄
防      1       防, 枋, 枕, 偽, 隧
妄      X       気
初      X
決      X       泥, 沫, 泯, 沸, 泱
央      X       史, 决, 吏, 向, 岔
習      1       習, 跫, 笥, 筥, 筍
練      X       踝, 踴, 閥, 諌, 錬
近      X
化      1       化, 价, 仙, 他, 伊
福      1       福, 熕, 褌, 複, 磆
北      1       北, 把, 地, 托, 叱
便      1       便, 峺, 悗, 栲, 僊
版      X       放, 施, 倣, 昨, 站
使      1       使, 俚, 便, 候, 俾

Three months ago, I started my experiment project when I collected kanji data. I then worked on the project an hour or two from time to time. I obtained my first results earlier this week. I was extremely happy of seeing results at last. It was difficult to keep on track because sometimes, I didn’t work on the project for days or weeks. My initial results outperform the current Tomoe recognizer, with some limitations, that I will develop later. I will publish my work and give more details about it in another post.

I’ve been living in Japan for 6 months already. I must say, wow, time flies very very fast. This is abroad for me so there’s always something new to do or to see. I don’t have much time for myself or personal projects. But I have (very) slowly been working on some experiments on handwritten Chinese character recognition. Stay tuned.

I will graduate from my engineering school in June (masters) so lately I have been thinking to what I want to do in the future. As I said, times goes very fast. It’s thus very important to fulfill myself during the 8 hours that I spend at work, whatever it may be. So far, I’m pretty sure that I don’t want to work as a software developer for a private company. I want to do something more challenging and in connection with research. Most importantly, I want to LEARN new things every day. Options I’m considering are creating my own company, becoming a research engineer in a private lab or starting a PhD. I’m glad because I think I have narrowed down the fields I would like to work in, in the future. I would like to work in the fields of Machine Learning and Pattern Recognition, especially handwriting recognition, natural language processing (NLP) and speech recognition.

I uploaded some of my pictures of Japan. I still have many others that I need to sort out but if you are interested, take a look.