Table Extraction was developed as part of my final year project at University to extract tabular data from images. The main motive behind it was to extract prayer times given a timetable and later develop an application to automatically send prayer notifications on your phone.

After analysing past research in the computer vision area, I decided to opt for a linear based solution given that most prayer timetables include all the table lines for easier reading. As for the technologies, I opted for Python and the OpenCV library due to their popularity in the field, and Tesseract as the OCR engine.

I first started by using image processing to remove noise from the image, convert to grayscale and thicken lines to identify the table region in the image. Using contour detection, the largest quadrilateral is identified in the image and extract (warp) the table.

Next, image binarization is applied to only retain table text and lines. Linear kernels are used to detect vertical and horizontal lines, which are then merged into a single image. Performing contour detection will then identify all the cells in the image.

Finally, an algorithm was developed to assign cells to their respective rows and sort the data from top-to-bottom, left-to-right. Each cell is parsed through the Tesseract OCR engine, and the entire table is returned in JSON format.

The framework was tested against multiple prayer timetables and achieved a cell detection accuracy of 86.8%. This is impressive considering my inexperience in computer vision prior to this project, and demonstrates above-average accuracy of my solution.

Unfortunately relying on table lines can cause a significant drop in cell detection if the input image is blurry. A possible solution would be to remove table lines all together and instead use space information between text to identify individual cells, but further research is needed to validate this theory.

For future use, I implemented a simple Flask API that accepts images to be sent as a POST request and returns the JSON data to the user. I then packaged the project in a Docker container for easier set up and deployment, 

with the image released on the Docker Hub

Personally, I was able to benefit from the research behind the framework to explore computer vision concepts and apply them to automate a task I would have otherwise performed manually. The project was challenging but rewarding at the same time. Developing various algorithms from scratch was nerve-wrecking at times but seeing everything come together in the end was a thrilling and joyful experience. Most importantly, I was able to take the theory behind concepts and ideas from different university modules and apply them to solve a real-world problem.

A line-based framework to detect and extract tabular data in JSON format from raster images using computer vision and Tesseract OCR.

Table Extraction

I was approached by George, the client for this project, to produce a subscription platform where GCSE maths students can practise challenging questions to boost their grade. As a maths teacher he wrote many tailor-made questions over time and was looking to build a platform to share his resources with other students, teachers and schools.

Initially, the client attempted at building the website themselves using WiX, but the design was not appealing and usability from a user perspective was limited.

After receiving the requirements and understanding the desired layout of the "questions" page, I began working on a modern design to suit the high-quality content that will be available to users. Crucially, it was important for navigation between topics, sub-topics and questions to be close to each other to allow easy and quick transitioning between different topics.

An admin area was also added to aid the creation of questions. Different features were implemented to help with the management of questions, such us drag-and-drop to change the order of items and a toggle to hide work-in-progress sections of the website.

Each question includes different options to restrict visibility to paying subscribers and to display additional icons from the main screens, such as calculator questions, exam questions and difficulty level. Moreover, the Dropbox API was implemented to better integrate with the client’s workflow, such as linking to video solutions by simply selecting the resource from an existing Dropbox library.

To allow for payment processing, the Stripe platform was decided as the best fit for the client’s needs. The API was implemented in the backend to ensure correct and secure processing, with automated emails sent out once a subscription is successful by registering a webhook. The Stripe Customer portal was also used to allow users to manage their subscription anytime directly from the website.

With this project I was able to apply the skills I learnt over the past year on full stack deployment, with the added benefit of learning email delivery, payment processing and webhooks along the way. More importantly, this project was the key to discover and unlock the full potential of my abilities, feeling more confident on producing robust and reliable code to tackle complex tasks.

Working alongside George was very important to ensure the final product meets his needs and to clear up any ambiguities in his requests. Not only did this shorten development time, but also saved time when it came down to writing code and kept the focus on the most important tasks for the website. Overall, the project was a success and the client is happy with the result:

Founder, Maths Advance

It was my pleasure to have you doing the coding Abdullah. I think it's fair to say it would have never got off the ground without your help.

A subscription platform for GCSE grade students to solve unique, tailor-made challenging maths questions along with video explanations to boost their grade and achieve better results.

Maths Advance

Project Management was an application build as part of the JVM Programming Languages course at University. The aim was to introduce us to programming languages designed to run on the JVM and encourage remote group collaboration during the Coronavirus lockdown.

As the member of the team with the most programming experience, I was selected as the lead developer. Responsibilities included:

Create an initial codebase and maintain the project on GitHub

Overseeing the project, ensuring that tasks were completed on time

Organize regular meetings with my colleagues to schedule tasks for the day/week

Help out whenever someone was struggling, providing pseudocode or fixing bugs

With responsibilities shared amongst us, I began setting up a project to support Java, Kotlin and Scala. To make setting up easier for my peers, I used Maven as the dependency manager.

My task was to produce the critical path finding algorithm in Kotlin and Scala. For the former, an object-oriented approach was used, with the process similar to the one described in 

. The main reason for choosing this implementation is the extra information that it produces: the output of this algorithm can tell which tasks are in the critical path, which tasks can be delayed without affecting the project, and the expected duration of the entire project.

Overall, the algorithm works successfully and is capable of identifying a critical path correctly. During testing I found some issues, which I was able to debug and fix.

In regards to the coursework as a whole, most features work correctly and do have unit tests to ensure nothing breaks when refactoring or adding code. The only exceptions are persistence and the GUI. Regarding persistence, the lack of tests meant we weren’t aware of a minor bug in the code until functionality was implemented into the GUI. More specifically, when creating a new team through the interface, the JSON file would not update automatically. Upon further inspection I found out that the “save()” method was not called when creating a new team object.

Due to the pandemic, we thought it was a good idea to use the git versioning system and host the code on GitHub to allow for easier collaboration and code sharing. Each of us had their own branch, working on a particular aspect of the project, and later we would merge the code into the master branch to combine everyone’s code. I have previous experience with git, so I’m familiar with the concept of committing code regularly with clear, concise commit messages.

However, my colleagues had little to no experience with this, so sometimes they would include multiple changes across a number of files in a single commit. This is something that could be improved in future projects and would be of great benefit in the workplace when working on a large codebase with dedicated members checking merge requests.

Generally speaking, version control did work successfully, and we were able to work on this project remotely, as a group, very well.

A JVM application for project managers to manage projects and teams, keep track of tasks and calculate the critical tasks and the expected duration of a project.

Project Management

 from London who loves crafting modern experiences for the web.

Java

Python

Kotlin

JavaScript

Laravel

MongoDB

GraphQL

Express

React

NodeJS

Typescript

Docker

These are the languages and frameworks I am most familiar with, and have used them in past projects. I love learning new things everyday and can pick up new technologies with ease.

Studio​ ​Operations​ ​Manager, Inition

Abdullah’s​ ​approach​ ​has​ ​positioned​ ​him​ ​as​ ​a​ ​great​ ​example​ ​to​ ​other​ ​students​ ​who​ ​wish​ ​to​ ​expand​ ​their​ ​skills​ ​and knowledge​ ​by​ ​showing​ ​initiative​ ​to​ ​contact​ ​and​ ​progress.

Founder, EasyFX

You did an awesome job and went beyond the call of duty. Thank you so much!

Table Extraction

Maths Advance

Project Management

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Table Extraction

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