Master Thesis of OCR using deep learning - Pavel Andrlik, 2022

Thanks for another citation by Pavel Andrlik, at the Master Thesis on OCR using deep learning.

University of West Bohemia
Faculty of Applied Sciences
Department of Cybernetics
(Czech Republic)

The author has been highlight my paper over the classification method choice on using Support Vector machine...[]

 Abstract of the Thesis

This diploma thesis deals with the problem of optical character recognition (OCR) using neural networks. I am focusing on improving text detection and OCR by fine-tuning an E2E-MLT scene text detector by training it on synthetic data which emulates real data. The model was fine-tuned on several datasets with synthetically generated data and real data, then the models were tested on one synthetic and two real datasets, one with the majority of the wild text, the second with the majority of TV news imprinted text. On the dataset with majority of TV news imprinted texts the fine-tuned models achieved improvement by decreasing character error rate from 52% to 31.6% word error rate and from 56.5% to 22%. It was also experimentally discovered that training models on synthetic data simulating real TV news images deteriorate detection and reading model capability on wild text data.

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What I am interesting is at the motivation side!

Full Thesis Can find at: https://otik.uk.zcu.cz/bitstream/11025/48953/1/Thesis___Pavel_Andrlik.pdf

My quick reflection on the motivation side!

The use case could also apply on some written paper for data collection such as on artist idea, random articles etc. we have a lot of handwriting or piece of writing printed that should also consider as collection on our language.

A compact deep learning model for Khmer handwritten text recognition

Bayram Annanurov, Norliza Mohd Noor 

Department of Computer Science, Paragon International University, Cambodia 

Department of Engineering, Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Malaysia

Abstract (of the Paper)

The motivation of this study is to develop a compact offline recognition model for Khmer handwritten text that would be successfully applied under limited access to high-performance computational hardware. Such a task aims to ease the ad-hoc digitization of vast handwritten archives in many spheres. Data collected for previous experiments were used in this work. The oneagainst-all classification was completed with state-of-the-art techniques. A compact deep learning model (2+1CNN), with two convolutional layers and one fully connected layer, was proposed. The recognition rate came out to be within 93-98%. The compact model is performed on par with the state-of-theart models. It was discovered that computational capacity requirements usually associated with deep learning can be alleviated, therefore allowing applications under limited computational power.

Link To the Page 

Optical character recognition system for Baybayin scripts using support vector machine

A new publishing related to SVM method on OCR case, "Optical character recognition system for Baybayin scripts using support vector machine" -  https://peerj.com/articles/cs-360/

Thanks for citation that to have more clearer that the method could work in some other cases.

This part is delight me and remind it back.

Abstract (of the paper)

 In 2018, the Philippine Congress signed House Bill 1022 declaring the Baybayin script as the Philippines’ national writing system. In this regard, it is highly probable that the Baybayin and Latin scripts would appear in a single document. In this work, we propose a system that discriminates the characters of both scripts. The proposed system considers the normalization of an individual character to identify if it belongs to Baybayin or Latin script and further classify them as to what unit they represent. This gives us four classification problems, namely: (1) Baybayin and Latin script recognition, (2) Baybayin character classification, (3) Latin character classification, and (4) Baybayin diacritical marks classification. To the best of our knowledge, this is the first study that makes use of Support Vector Machine (SVM) for Baybayin script recognition. This work also provides a new dataset for Baybayin, its diacritics, and Latin characters. Classification problems (1) and (4) use binary SVM while (2) and (3) apply the multiclass SVM classification. On average, our numerical experiments yield satisfactory results: (1) has 98.5% accuracy, 98.5% precision, 98.49% recall, and 98.5% F1 Score; (2) has 96.51% accuracy, 95.62% precision, 95.61% recall, and 95.62% F1 Score; (3) has 95.8% accuracy, 95.85% precision, 95.8% recall, and 95.83% F1 Score; and (4) has 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.

State of The Art Of KhmerOCR Implementation

There aren't many articles when we search on the Internet about KhmerOCR topic, I, myself don't find a lot as well.

Of course, I believe that there are some people or companies might quietly in implementing the solution for that but without any announce I believe my presume below are relevant enough for people to understand about current situation of Khmer OCR.

Let's share around "State of The Art Of KhmerOCR" today ;)

I could find that, there are about several Khmer OCR researches being published through some organization, website and universities.

Methodologies

When we talk about Khmer OCR, we suppose around the solutions to make any characters from scanned images of handwritten, typewritten or printed text converts into machine-encoded text.

Solutions on OCR system, mostly focus on:

• Pre-processing (usually is noise removal)
• Segmentation
• Line segmentation
• Character segmentation
• Recognition
• Mapping (Character Assembling)

And there are some methods already used for Khmer OCR in segmentation or recognition part such as

• Lagendre Moment Descriptor,
• Wavelet Descriptor,
• Hidden Markov model (HMM),
• Back propagation,
• Scale Invariant Fourier Transform (SIFT),
• Fourier Descriptor, Hole detection
• Template Matching
• etc.
• And (it seems) the last one is: Support Vector Machine (SVM)

Literature Review/History

I might miss some others but here are what I could find about what have done so far with this topic.

If you, guys, have know some more, please share to people through comment form. I will check and update.

1. The Khmer Printed Characters Recognition using Lagendre Moment Descriptor by Chey Chanoeurn et al got 92% of accuracy on 10 Khmer consonants including ប ព ជ ក ភ ណ ឃ ស វ and ឆ
2. 2005, The Khmer Printed Character Recognition Using Wavelet Descriptors by Chey Chanoeurn et al got the accuracy of 92.85%, 91.66% and 89.27% on 10 types of Khmer fonts in 3 different sizes.
3. 2008, The Khmer Segmentation for font Limon S1, size 22 by Ing Leng Ieng, PAN Localization Project got the accuracy of 99.11%.
4. 2009, The Khmer OCR for Limon R1 Size 22 by Ing Leng Ieng from PAN Localization Project using framing and Discrete Cosine Transform calculation for recognition based on Hidden Markov Model and got the accuracy of 98.88%.
5. 2011, The Khmer Optical Character Recognition (OCR) by Mr. Kruy Vanna using Fourier Descriptors, Component’s Holes, and Component’s Location got accuracy of 97.9% on 19 types of Khmer font.
6. 2012, The Khmer Printed Character Recognition uses combining of Edge Detection and Template Matching by Iech Setha et al for one font “Khmer OS Content” with font size of 36pt got accuracy of 99%
7. 2013, The Khmer Printed Character Recognition using Support Vector Machine (SVM) based, by Pongsametrey SOK for one font “Khmer OS Content” with font size of 36pt got accuracy of 98.54% (32pt = 98.62%, 28pt = 98.18%) with training set of font size: 32pt

The research No. 7, I did it and it's submitted at Royal University of Phnom Penh (RUPP). So it's not publicly publish any where yet.

Who are doing it nowadays

That's who I have known around in Cambodia only, it might be people who does some study abroad is also doing it. Anyway here what I have known:

• Institute of Technology of Cambodia (ITC), It seems, there're some continuing implementation of KhmerOCR there
• Royal University of Phnom Penh (RUPP) also doing some more researches on this matter through students' researches, thesis and with their lecturers.
• Open Institute (Open Forum, KhmerOS.info), I believe that this topic is still interesting by this NGO
• And there are some other individuals as well as I heard (?)

What's Interesting

One opensource OCR engine, Tesseract OCR, it's a completed engine from the image processing to recognition and its output.

What we need for our Khmer language works for it, we need to analyze "how to train" our dataset.

I also did some training for Khmer as well for Tesseract for some letters, it seems that the system is good to go but there are some thing we need to aware before as I posted a question here.
I will try to write a post on how to train some characters that I did before.

Few Training Char, All Are Error

 
Why Tesseract at this time?
Previous researches are mostly using their own combination of methods to solve various issue for Khmer language such as in segmentation or recognition but the pre-processing process (image processing) is also important for a real OCR system and its accuracy.
And I could see that Tesseract OCR is ready for all of that.

Is There Anyone Already Try for Tesseract?
Yes, you can search on Google, it has already been trying since 2009 per my search on Google and around.
And it might be already made by some universities or lecturers but remaining unclear for me.

So, Is There Any Ready Tesseract OCR for Khmer?
My presumed answer: No, I've never heard that there's a ready training set for Khmer yet to use in Tesseract OCR Engine.

But, just today, I checked again at the repo of Tesseract (14 January 2014), I saw some Khmer config is added (Files: Khmer.unicharset, Khmer.xheights), we need to test if they are working.

Therefore, Students, Lecturers, some NGO or community should take part to help this.

Conclusions

The OCR system is very interesting for people nowadays.

We are using Khmer Unicode since it established in 2003 in the Kingdom and with Unicode, we have Google translate recently. Then, Khmer OCR should be also solved somehow as well.

We need more people to do it, to help, to share and publish.

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Article Revision

• 23/04/2014: Initial the article

Remark: 

• If any mistake in above research, please alert me in comment.
• More detail of each research, please find the published paper to read in detail

KhmerOCR Development from ITC

There are few stakeholders are doing KhmerOCR, among them, researchers and students from ITC (Institute of Technology of Cambodia) are also doing it as well.

Here is one of a public presentation during Barcamp event about KhmerOCR introduced by ITC students/researchers.

Khmer OCR from Bill Jeer

There are other researchers from RUPP as well.