Background If you are anything like me, a salaried employee, you would have not worried about the money that the organization compensates you. At best, you would look at your salary slip, maybe glance through the deductions and make sure the amount mentioned after the deduction and the amount deposited in your bank match. You do a couple of times, which assures you that there is a match, and then you do not even look to match. You are happy if at the end of the month there is an SMS that your salary has been credited and that is it. Closer to your retirement or superannuation, especially if you are in an employment where there is no central/state government pension suddenly "money matters" especially because your monthly credits in terms of salary disappears and you need to plan or figure out how to try and fill that "monthly salary". This is when the deductions in your salary slip (which was not cared for all these years) requires your attention. Read on, if you a...
Background : For years, I had a habit of writing whenever I traveled. No laptop, no apps, just a pen, a notebook, and pages of cursive observations about airports, flights, fellow travelers, and the emotions that come with being away from home. Two decades later, I decided to digitize those notebooks and make them available on Kindle, eventually publishing them as Transit Tales: Between Airports and Emotions: Travel Blog 20 Years Too Late (View on Amazon). As a first step, I assumed modern LLM-powered handwriting recognition would effortlessly convert the pages into text. Instead, the journey led to an unexpected discovery: handwriting recognition is still far from "solved." The apparent success of modern handwriting recognition is increasingly a combination of imperfect OCR and powerful language-model reconstruction. What users see is often not what the system read, but what the system inferred. Handwriting Recognition: Better Than It Looks, But Not For The Reason You Think ...