Google Summer of Code, Weeks 6 and 7 – Detecting Colored Subtitles

Google Summer of Code, Weeks 6 and 7 – Detecting Colored Subtitles

Till this point, I have a system which works well for burned-in white subtitles and generates a timed output file. The next step is to add the same support for colored subtitles too.

The HSV Color Space

The HSV color space, and the Hue component (H) in particular, is an excellent representation of the exact color value of a pixel. The normal RGB space requires 3 values to represent the color, whereas the H component takes a value in the range of 0-360 and gives the necessary color information.

You can read more about the color space here.

The chart below shows how the values of H vary for different types of colors.


I exploited a conversion from the RGB to the HSV space in order to detect colored subtitles. Just like there was a luminance threshold in order to detect white subtitles, there is a threshold around the range of the user-specified hue value in order to detect subtitles of a particular color.

This hue based thresholding, along with the existing vertical edge dilation was used to detect subtitles of a particular color.

Color options in the program

The program has 7 predefined color names. The first and most prevalent case is White, the detection of which is luminance based. The other 6 are equally spaced in the hue value range. The colors, along with their hue values, are:-

  1. Yellow – 60
  2. Green – 120
  3. Cyan – 180
  4. Blue – 240
  5. Magenta – 300
  6. Red – 0

Each of these colors can be specified along with the -subcolor option. For example:-

ccextractor video.mp4 -hardsubx -subcolor yellow

In addition to these preset values, there is also the possibility to supply a custom hue value. This value is a custom value between 0 and 360 (not included) which can be supplied to the subcolor option, and could be of help to users who want to extract subtitles of the precise hue value in their stream if it fails to meet one of the presets.

Local Adaptive Thresholding

In addition to detecting colored subtitles, I was also able to improve the detection of white subtitles using local adaptive thresholding algorithms, and Sauvola Binarization in particular. This was an additional step which marginally improved the quality of results for white subtitles (which always have a pixel value greater than their surroundings), however could not be applied to colored subtitles in all cases due to a wide variety of contrasting backgrounds.


Google Summer of Code, Weeks 4 and 5 – Determining Subtitle Appearance Time

Google Summer of Code, Weeks 4 and 5 – Determining Subtitle Appearance Time

So far, I have been able to successfully extract white colored subtitles at an interval of 25 frames, and the output looks decent. However, I need to now actually created a timed transcript (e.g. an SRT file).

Original Plan

I had originally intended on having two strategies to determine subtitle time, which I had described in my proposal as:-

  1. A linear search across the video at a certain interval. Whenever a subtitle gets detected, a binary search will be performed in a window around that frame. Using this, we will detect the exact time of the beginning and the end of the particular subtitle line. This will be of benefit in sequentially processing a file (possible use case of processing a live stream as it is being recorded).
  2. If the entire video is already available to us, instead of doing linear search which will involve a lot of processing overheads for frames in which there are no subtitles, we can directly do a binary search on the entire video to detect subtitle lines. We will get the exact timing of the line as described above, but the overall processing will be faster

However, neither of them were possible, due to constraints which I had not originally anticipated.

FFMpeg Constraints

It turns out that binary search was not a viable option at all, because I could not arbitrarily seek to a timestamp in a video using the FFMpeg library. The closest thing which I could do was seek the file to the nearest I-frame and then iterate through frames to the desired timestamp and then reconstruct the needed frame. However, in a binary search, the whole point of which was to optimize the search, this way would create a massive processing overhead and high redundancy in reconstructing frames during the search. Instead, a linear search with a specified step-size seemed a much better option.

The problem that I described is fairly well documented online:-

New Plan – Efficient Linear Search

I decided to use a linear search across the video with a specified step-size, which was a parameter called the minimum subtitle duration. I set the default value for this as 0.5 seconds, which seems a reasonable assumption for most subtitles.

I also needed to convert times to a single format (milliseconds), from the various different time bases that various different video streams could have. From here, I iterated through the video and sampled frames at regular intervals. The decision that a subtitle line was the same as the last encountered one was when it’s Levenshtein distance was very low. This was necessary in order to combine successive detections which were off by a character or two, which happens quite often due to the natural noise present in the video stream. Whenever the detected subtitle line ended, I would encode it with the seen times.

Integrating with the CCExtractor Encoder

It was really easy to integrate the calculated time with the CCExtractor encoder structure (which with itself brought full output parameter functionality). All I had to do was call two functions at the appropriate times in my code:-

add_cc_sub_text(ctx->dec_sub, subtitle_text, begin_time, end_time, “”, “BURN”, CCX_ENC_UTF_8);
encode_sub(enc_ctx, ctx->dec_sub);

That says a lot about how well written and modularized the existing library is.

And oh, I chose the subtitle mode ‘BURN’ myself. It stands for burned-in subtitles xD.

Example Output

The SRT output for the video at (A Gerard Pique interview), looked as follows:-

00:00:00,000 –> 00:00:06,919
Well, they’re both spectacles,

00:00:06,921 –> 00:00:08,879
NBA basketball as well as football here.

00:00:08,881 –> 00:00:12,879
It’s a spectacle for the fans, they enjoy it and see

00:00:12,881 –> 00:00:13,919
something different.

00:00:13,921 –> 00:00:19,919
And I think a player like Messi could be compared to Curry

00:00:19,921 –> 00:00:21,919
in the USA,

00:00:21,921 –> 00:00:24,879
because they have created something special

00:00:24,881 –> 00:00:26,999
something not seen before,

00:00:27,001 –> 00:00:30,919
and something that makes people excited, ecstatic.

It looks pretty good, and the times are pretty close to perfect, with some variation at the extremes due to those edge frames not being processed. A lower value for the minimum subtitle duration will give even more accurately timed results, but will take a longer processing time.

Google Summer of Code, Weeks 2 and 3 – Recognizing White Subtitles

Google Summer of Code, Weeks 2 and 3 – Recognizing White Subtitles

These last two weeks were slightly challenging owing to the fact that I had to learn a lot of new things in order to complete my tasks.

Setting up the HardsubX expansion in CCExtractor

Before I could get started on diving deep into writing code, I needed to organize and setup the workflow of all the new code which I am supposed to write throughout the summer into the original program. This included parsing input parameters for the new type of extraction process, creating and organizing new files in the source code, and changing the compilation settings and dependencies to match what I would need for my pipeline.

The pipeline essentially comprises of the following entities:-

  1. The ‘main’ file
    Handles the parsing of parameters and initializing the required data structures.
  2. The Decoder
    Gets the text of the burned in subtitle in the video by processing it
  3. The Timer
    Gets the precise timing of each extracted subtitle
  4. The Encoder
    Converts the output of the decoder and the timer into a standard output format such as a .srt(SubRip) file

I created separate files for each of these entities and their helper functions, along with one shared header file which would allow the internal librarization of the files (being able to use functions from one in another), as well as the potential external librarization (being able to be called from the main CCExtractor library).

You can view the project repository at The new source code files are in the ‘src/lib_ccx’ directory and have the ‘hardsubx’ prefix in their names.

Processing a Video Stream in C

The very first step when trying to get subtitles from a video frame, is to actually get those video frames themselves and store them in a data structure in the context of the program. The FFMpeg library is the comprehensive open source media processing library in use today. I am using its C API to process the input video stream.

I needed to store the video stream format and codec information in the program context. Then, out of the many different kinds of streams present in the media file (video, audio, captions, others), I needed to find the ID of the video stream and then process only its packets. Every video stream packet is then decoded and the image content extracted and stored in a Leptonica PIX structure (for compatibility with Tesseract OCR). For the sake of efficiency and avoiding redundancy in frame extraction, I extract frames at an interval of 0.5 seconds, which I have assumed to be the minimum time that a subtitle line is present in the video. This number can be fine-tuned based on the real situation, but some threshold is required in order to avoid the massive processing overheads of reading every single frame in the video.

In a nutshell, the process goes like this. FFMpeg gives me the video frames at a certain interval, and then I further process them to detect subtitles.

Example frame:-


Detecting Subtitle Regions

The detection of white subtitle regions involved two steps:-

  1. Luminance based thresholding
  2. Vertical Edge detection and Dilation

The Luminance (L) of a particular pixel represents the ‘whiteness’ of the pixel. The closer it is to pure white, the higher is its luminance. When aiming to detect white subtitles, luminance based thresholding is useful because if we binarize the image in such a way that only regions of high luminance are retained, then all of the white subtitle regions will be retained (with possibly other white objects/artifacts too). This thresholding is done to narrow down the search for the candidate subtitle region.

Thresholded Luminance image:-


The second part of the subtitle detection pipeline is the detection of vertical edges in the image, which is done by a vertical Sobel filter. This method is effective because subtitles have a high density of strong vertical edges in their region, due to the alternating white foreground letters and the non-white background. The edge image is then dilated with a horizontal structuring element in order to get the rough region of the subtitles.

Vertical edges:-


After dilation and thresholding:-


The final subtitle region is determined by taking a bitwise AND of the two feature images described above, i.e. regions which are both wide and also have strong vertical edges. Both these features are typical of white letters in the subtitle line. In some cases, one step may not work well. For instance, if there is a white background, then the thresholded luminance image will not be an accurate representation of the subtitle region. Also, if there is an object with lots of vertical edges near the subtitle region, the edge image will not be an accurate representation. But using both of them together give us a high likelihood of accurately detecting the subtitle region.

Subtitle Recognition / OCR

Once the subtitle region of interest has been detected, the actual text needs to be recognized using OCR (Optical Character Recognition). The intuitive choice to perform this task was the Tesseract OCR library by Google, which has already been previously used by CCExtractor to recognize DVB subtitles (predominantly used in Europe) which essentially comprise of a subtitle bitmap being overlaid on the video frame. An OCR essentially works using character and word classification based on stored labels on trained data. In a layman’s terms, you show the OCR engine 1000 images of the letter ‘a’, and it learns to recognize the letter ‘a’ the next time it sees it.

For the output image of the previous steps:-result

Tesseract’s Detected text : “Well, they’re both spectacles,”

All I need to do is pass the binarized image containing only the clean detected subtitle text to a Tesseract API handle and it returns the recognized text to me. Pretty cool, right?

Over the course of the summer, I will have to use the Tesseract API extensively, as compared to just directly making a call to get the recognized subtitle text. I will be using advanced Tesseract features such as the per character and the per word confidence ratings in order to refine and improve my text classification output. A common use case for this would be to root out simple misclassifications such as ‘giape’ instead of ‘grape’ in the recognized text, and to get the overall output to have the highest probability of being correct.

What’s Next?

The next thing that I need to work on is to accurately and optimally determine the time that each subtitle line was present in the video. This will involve seeking the video around the neighborhood of the frame of the originally detected subtitle, and then determining when that particular subtitle line appeared in the video for the first and the last time. A potential problem with optimizing this seems to be the fact that ffmpeg does not allow straightforward seeking to a given frame number or a timestamp, and I will have to manually seek to the desired location from the nearest I-frame (You can understand this problem better by understanding the GOP structure of video frames, explained here).

Here’s looking forward to weeks 4 and 5 and the mid-term evaluation which is on the near horizon. I’ll keep posting my progress, right here. Cheers!

Google Summer of Code, Week 1 – The Bug Hunt

Google Summer of Code, Week 1 – The Bug Hunt


I have just finished my first week of the Google Summer of Code (GSOC) program with the CCExtractor organization. This is the first of thirteen weeks during which I will be working on developing a system which will be able to extract hard (burned-in) subtitles from a given video, adding to the current functionality of CCExtractor which extracts soft subtitles, i.e. those which are part of the data structures of the video stream but are not part of the video itself. This process will involve subtitle text localization followed by optical character recognition (OCR). In a common man’s words, this will make the computer understand which letters are part of the subtitles as compared to the series of pixels it originally sees in the video frame. These recognized letters can then be written to one of many popular subtitle formats such as the SubRip format (with a .srt file).

You can read my project proposal here.

The Setup

I am working from my university accommodation, with my development environment being Linux (Ubuntu 14.04). My setup can be seen in the featured image on this post, with two OS’s running side by side. I write all my code in Ubuntu and have GitHub or any docs open on the left screen in Windows 10.

A great thing about GSOC is the complete freedom you are provided, as long as you work for the stipulated amount of time. It is like a full-time job, without the fixed hours or travel times. As long as I finish my assigned tasks and work for 40 hours in the week on my project, I can do whatever I want with my spare time. This week has been really fun in that aspect. I have had the perfect blend of working and enjoying my summer at the same time. In fact, if you look at the picture of my setup, you will see League of Legends open in the background (I was about to play a game at the moment of taking the picture). I would often update a pull request and run tests on it, and play a game while the tests ran, and then get back to work immediately after. I never felt short of time to enjoy my personal life while working on the project at the same time. I am working on a computer vision problem which interests me and is fun, while getting enough time to game or go out, and earning $5500 over the summer. What more could I ask for, really?

The Bug Hunt

The first week of the coding duration of the program was devoted to trying to fix existing bugs in the code. The rationale behind this is that all developers work together for a week to try and fix as many bugs as possible following which there is a new, improved release of CCExtractor (version 0.81 to be precise). This gives all developers a much more stable/bug-free version to work with for the remaining part of GSOC.

In addition to fixing bugs, this week also gives a lot of time to actually get acquainted with the code in depth, which was particularly useful for me because I need to seamlessly integrate the pipeline I will develop with the normal workflow of the existing program, for which I need to know what is happening in the code and what parts I need to use and edit. And fixing bugs is a fantastic way to achieve this higher level of acquaintance with the code base.

I was assigned 6 bugs to try and fix during this week. I successfully managed to fix 3 bugs. The bugs assigned to me were:-

  1. Case fixing for teletext subtitles (Fixed)
  2. Seeking DVD video using the IFO (information) metadata file
  3. DVB subtitle extraction not working for a Spanish channel
  4. Issues with timing is ISDB (Brazilian) subtitles (Partially Fixed)
  5. Missing subtitles in a Korean broadcast
  6. Very high RAM consumption by the program (Fixed)

I also managed to fix another bug which was reported by David Liontooth of the Red Hen Lab at UCLA and had originated due to a previous pull request of mine. This entire process educated me a bit about how the maintenance stage of the software development life cycle is the one which involves the most effort. It is easy to write hundreds of lines of code and make something work at first glance, but with usage over time, we inevitably discover problems which need to be fixed. And in the open source world, it is good form for the person who wrote the part of the code which is causing problems to be responsible for it and work on a fix.

The Major Fix – Reducing RAM Consumption by 180 MB

In my opinion, the most significant issue which I was able to solve this week was reducing the memory consumption of the program by 180 MB.

A fellow developer had reported very high memory consumption by every instance of CCExtractor in an application he is building. After a lot of analysis, I narrowed down the problem to the fact that around 180 MB of space was being allocated statically for EPG (Electronic Program Guide) data for two months in every instance, even if it was never being used. Once I had this pinpointed, I submitted a fix which changed the allocation to only when needed. Easy enough right? That’s what I thought too, and I ran tests on my pull request, which did reduce memory consumption from 200 MB to around 20 MB. Bizarrely, the fix was causing segmentation faults (accessing invalid parts of the memory) for a set of test cases. I had no idea why. I spent the whole next day on tracing variable initializations throughout the code which could be causing the issue, and finally saw that at one small line of code, the wrong context was being passed to a function causing a required value to not be set. You can take a look at the technical details here.

I managed to fix the issue by passing the correct context later, but the baffling question was that why did it work earlier even when the wrong context was passed? As it turned out, when 180 MB of memory was previously being allocated (and set to 0 entirely), the value which was later uninitialized due to having an invalid pointer actually pointed to one of these 0’s in this huge chunk of memory, thus getting a valid value, and not causing anything to break. This was a perfect example of how a bug may just sit there, hidden and not break anything and come up later in the development life cycle and completely mess with your mind. But anyway, it was fixed and the program was now running as expected, while being 180 MB lighter on RAM 😀

An excerpt of the conversation on the GitHub issue page is as follows:-


It made me really happy to be able to help out with a fellow developer’s application. After all, that is what open source is all about. We come together and use the cool things that each other have developed, and move ahead to create something even better. It may be something as small as writing a small ping pong game or something as big as writing a deep neural network API such as Caffe which drives cutting edge features of even giant companies like Facebook, every little bit counts, and makes the code that the world uses better.

Moving to Week 2

In week 2, I will actually commence work on my project. I need to work on a frame extraction and pre-processing module for white colored burned-in subtitles. I look forward to making it happen, and I will update my progress right here. Cheers!

My Guitar Story So Far

My Guitar Story So Far

Yesterday, I bought my very first guitar. After a few months of learning and playing my friends’ guitars, I decided that it was time I got one of my own. Ultimately, when I packed up my newly bought guitar (A Metallic Red and White Yamaha Pacifica 012 Electric) and got into a cab back to college, I started to think. Think about how certain stories and memories have grown around my first flirtations with playing the guitar. It is quite remarkable how such significant moments in your life can be intertwined with this instrument that you play. Well, guitars are pretty personal things after all.

How It All Began

It was a pleasant day in February 2015. The girl who I was seeing then, had joined this guitar class pretty close to college, with a few of her friends. I think I did a random YouTube search one evening, about the very basics of guitar playing. I borrowed a friend’s guitar, a 11 year old Lewis acoustic guitar, which was the first guitar I ever played. I tried out a few things, and liked the feel of it. As a kid, I used to play the keyboard a lot, and I felt that I had a little bit of a knack for instrumental music, and I was able to rapidly pick up the basic stuff which my then-girlfriend would learn and later tell me. One day while walking with her, when she was supposed to go to class, I impulsively decided to go to class with her and actually signed up for it. Such little things, it is funny how often they end up being the big things.

Baby Steps

It still feels like yesterday. Those very first classes, those very first nursery rhymes which I played. “Mary had a Little Lamb” was probably the first rhyme I learned to play. With more classes, came more songs, as I slowly began to explore the left extreme of the fretboard and all the sounds which it could produce. Soon enough, I could play basic leads for any song which I wanted to after a short while. A little memory which I have is spontaneously playing most of “My Heart Will Go On” from Titanic. After a while, I learned the very basic chords and introductory rhythm guitar, and tried to play “Give Me Some Sunshine” on its own. I was always enthusiastic to learn ahead and I used to try barre chord versions of the song even before I could play the D open chord properly. Even though it didn’t sound great then, I enjoyed what I was doing, and it was good fun.

A Temporary Lull

Just like life in itself, my guitar journey so far hasn’t been completely been on a path which was covered in rose petals (a lot of it has been that way though, or at least felt like it). One particular low that I remember is a point when a lot of negativity had crept into life in general. It was around end semester exam time, things weren’t great with my then-girlfriend (some of our personality and ego clashes managed to find their way to the fretboard too, like she would want to be better than me, in a competitive and unfriendly way, at playing and so on) and there was a complete lack of time to do anything creative (Yes, Sem 2-2 of Comp Sci at IIIT-H can get pretty tough to handle). Ultimately exams ended, I survived the semester but the good part of my relationship did not, and in that moment what I really needed was a much needed break at home, which also ended up being a temporary break from the guitar.


The semester ended. I went home for a couple of weeks and came back to college in order to begin my summer research work. The summer also brought into my life two new friends, with whom the first proper conversations I had were completely due to the fact that I was learning/playing the guitar. The first one was a guy who I will refer to as Scouse (Liverpool fan), whose guitar I have played the most till date. It was a Santana acoustic guitar which he got around 2010. In the summer, when my roommate left for home, his bed was where the guitar lay if I was not playing it. Even when I play a newer, more expensive acoustic guitar, I still do not get the same comfort or sound that I got from Scouse’s guitar, on which I learned most of what I know about playing the guitar today. I also had a buddy to talk about the guitar with now, and we shared what kind of music we were playing, and he taught me little techniques which were very useful later. I shared some of the music/techniques which I was trying out and which he did not know yet. I used his guitar extensively ever since summer until I got my new one, during which I managed to break its strings a couple of times (twice in a day, to be precise). I really have to give it to him to never complain about this guy just coming and taking away his guitar for hours at end.

The second friend who I made over the summer asked to be called Batgirl in this blog post. It so happened that she had signed up for the same guitar class which I used to go to, and I had only started talking to her then. After the temporary lull, I picked up the guitar again and started going to class again, with her. She was a really fast learner and did in 4 classes what I could in 6, and played the flute too. Having someone else learning with you and getting me back on track was a very important thing at that time. Rejuvenation, in a guitar perspective, was complete. And the friendship did not stay limited to just playing the guitar or going to class. She happened to be a really nice person who I get along really well with. But it is interesting how such a friendship can emerge out of something as seemingly insignificant as having a first conversation in person due to going to a guitar class. Like I said, the little things become the big things.

As of today, I have plans to jam with both Scouse and Batgirl. That should be fun! 😀


Throughout summer, apart from the one day in the week when I actually did some kind of academic work, I was free all day. Apart from playing League of Legends and watching the occasional movie, all I did was play the guitar like mad. When I reached a stage where I could just look at the chords of any song and then play it and sing along, it got downright addictive. It was pretty normal for me to play all night, 8 hours at a stretch, until my fingers just couldn’t take it anymore. I could now relate to a verse in “Summer of 69”, which says that the singer “played till his fingers bled”. My internet search auto-complete suggestions were now littered with guitar chords and tabs. I discovered that I could sing a bit too, something which I was always afraid of all my life before I entered the world of rhythm guitar.

Another friend of mine (let us call her IO), demanded that I record myself singing while playing the guitar. I remember the very first recording that I did was a basic rhythm version of “Let Her Go” by Passenger. As I did more recordings, I slightly improved both my playing and singing skills, and kept on trying more songs, sometimes on demand (only by girls though, I wonder why :P). DJ, IO’s usual partner-in-everything, asked me to play a couple of songs for her too. The size of my Guitar Recordings folder slowly started to increase, even though I was making them using my phone’s sound recorder and they were not the best quality audio. I sang “Maa” from Taare Zameen Par for Mom and “Papa Kehte Hain Bada Naam Karega” for Dad on Father’s Day. Among other songs, a few that I recorded in the very early days were “Tanha Dil”, “I Won’t Give Up” and “Tumse Hi”.

Even when the new semester began, and I shifted into my room-to-be for two years (a single room now), I got hold of Scouse’s guitar and started playing. On occasional days when I had no class, I managed to play for 11-12 hours at a stretch. I could now do things which I could not do smoothly earlier, like quickly transition between barre chords across the fretboard and play different styles. I tried really difficult stuff like Amin Toofani’s “Gratitude”, and learnt new things while trying to play it. And I enjoyed every bit of the learning curve. Now that I could play a variety of music with a reasonable degree of comfort, I decided that it was time to invest in a good new guitar of my own. And that brings us to yesterday.

Buying My Own Guitar

The experience of actually buying my guitar was an interesting one too. I had only practiced on acoustic guitars all this while, even though I had played electric guitars at my class (which I did not particularly like, even though I did not exactly hate them). Initially I thought I would by a semi-acoustic or an electro-acoustic guitar. I went to a shop in Madhapur and tried out all the kinds of guitars which they had. It did not take me long to make up my mind about buying an electric guitar. They felt, good. And add to that the possibility of playing and exploring various kinds of music. That had me sold. After hours of googling around and trying to see what I should actually buy, I decided that I wanted an electric guitar and the Pacifica seemed to fit my needs the best (Close comparisons could be drawn to the Fender Stratocaster and an entry level Ibanez model). The next day, I got funds transferred to my bank account and headed out to a place in Begumpet called Musee Musicals. I asked Batgirl if she would come along, but she said that it would take ages to get there in rush hour traffic, and asked me to send pictures before buying instead. And oh boy, she was right about the traffic. I started out from college at around 6 PM, and midway through got stuck in bumper to bumper traffic, and reached the Google Maps marker only at 7:30 PM. As luck would have it, the marker was incorrect and the actual store was somewhere else. I called up the store and after half an hour of miscommunicated directions and more traffic, I managed to find the right place. I walked into the store, and there it was, in all its metallic red and white glory. I touched it, it felt good. It felt right. It sounded good. And it looked cool. Scratch that, super cool, like it made me want to play it. And soon enough, it was on its way to its new home, along with the complete package of an amplifier, a strap, a capo, cables, its bag and other guitar related paraphernalia. I had my very own wannabe rockstar starter kit.

Testing It Out

I came back to college. The moment I entered my wing, my friends saw me carrying this giant electric guitar package, a guitar bag hung over my shoulder and an amplifier in my hand. I saw their eyes light up when they realized that I had gone ahead and actually bought the guitar which I had been going on and on about for the last few days. I unpacked and set everything up, as my friends waited with expectation. The first strum sounded good. And then, I put the amplifier into overdrive mode. The instant I strummed the next note, everyone lost their minds. “Ohhhhhhhhhhh!”, they went, it sounded like a proper rock tone. And then I plucked the next note and bended it. “OHHHHHHHHHHHH!”, was the reaction. I don’t think I had had such a giant smile on my face for ages. Scouse came and checked it out, and was awestruck too. He played an electric version of “BC Sutta” which made us all laugh, followed by the intro lick of “Nothing Else Matters” which sounded so damn authentic. Later, I showed it to Batgirl too and she thought that the guitar was pretty damn cool. She made me play the intro lick of “The Diary of Jane”, and the bend at the end of the lick made her go “Whoaaaaa! :D” too. It had only been three hours, but I had started to love my new guitar already. This guitar journey has just begun.

A Strange Road Accident

A Strange Road Accident

I had a strange day.. Finished my last board exam and headed home by an auto-rickshaw.. On the way, a 7-8 year old child was walking on the road with his mother and a friend of hers.. He was listening to music on a mobile phone and ran blindly across the road.. My auto-rickshaw collided with the child who suffered multiple injuries and lay on the street, unconscious.. A mob gathered around us and had it not been for me and the auto-wallah rushing him to the hospital (the accident took place right in front of the Civil Hospital in Wardha), the auto-wallah would surely have been beaten brutally and his auto burnt.. We managed to get him to a bed of the hospital where he lay helplessly, now awake and crying.. It was clear that he had suffered a break in his knee joint (It looked limp).. A doctor came around 3-4 minutes later.. She looked at the kid and advised his mother to get him admitted.. Shockingly, the mother said that she couldn’t as she was in somewhat of a hurry.. The doctor then went on to stress the fact that the kid’s knee was fractured and he couldn’t possibly walk in that state.. The mother reluctantly agreed to have him admitted after being insisted to do so by almost everyone present.. There was a police inspector there taking a statement from another accident victim.. He advised the auto-wallah and the mother to not say that it was a road accident, as it would involve some police paperwork, which he seemed to want to avoid.. All I did was try and console the kid that it was going to be okay.. When I felt that I could do no more, I paid the driver and went home in another auto-rickshaw..
Life is strange sometimes..

A Hero Comes Home

A Hero Comes Home

The floodlights were on, the pitch was glistening
The whole world watching, the players listening
To the roar of the crowd as they walked out that night
On what was supposed to be a footballing delight

A hero returned, to his home which once was
He was greeted by cheers and thunderous applause
Thousands of fans, singing his name
It was just like the old days, almost all the same

But this night he was, on the side of the foe
Even if on the night, he was reluctantly so
He lined up against old friends, it was what he had to do
Because the foe kept food on his table you know

The game kicked off, the battle began
The quest was on to see who can
Emerge victorious on the biggest stage
And in doing so write a page
Of the famed pages of footballing history
But just who would win, was a total mystery

The giants clashed, it was an epic in the making
Every player on the pitch knew how vital was taking
Any half-chance which may come his way
To help his team take the next step to a Wembley day

The game went on, the home team went ahead
On the night of champions, first blood was red

Then came the moment, which changed it all
A home player jumped in the air for the ball
But the referee did not like what he saw
And the player was sent off, distraught

The momentum shifted, the foe now attacked
And with a touch of genius, they pulled it back

The fans still roared, singing in the night
As the home team put up, an incredible fight
But in the end, it was to be in vain
A night of magic, turned into one of pain

The clinching moment, was written in the stars
The hero did what he had to do
He latched onto the end of a low pass
And struck a blow into every home fan’s heart

He never celebrated, out of heartfelt respect
For he remembered his roots, where he grew
As a player, and as a man
Showing the world, what he could do

The game ended, the final whistle blew
The home fans still sang, for they knew
Their team had made them all proud
And their voices never ceased to sing aloud

They still cheered for him, their hero of old
Of this magical night, stories will forever be told
He turned back the years, showing his desire
To create magic on the field, for all to admire

The hero was doubtless, the star of the game
Battling with emotion, he walked off the stage
And with his heart pounding, as he did so

He could still hear the fans singing, “Viva Ronaldo”