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SecondCopy — Mode A is alive

I've got the Misrecogniser running end-to-end. The app now routes OCR + what3words + Ezra (GPT) into a three-line caption that sits comfortably in the APD register—grounded in what the camera sees, nudged by place, and allowed to be a little sideways. It will miscaption and misrecognise; it will also land on those odd, enlightening phrasings I wanted.

What’s happening on each label

Photo → OCR: pull concrete nouns/verbs from the page/scene.

w3w: fold in 1–2 words from the location (no slash soup).

Ezra prompt: ask for three lines, lowercase, field-note tone; line 2 must anchor to at least one OCR keyword.

Post-process: de-fluff, enforce anchors, keep it legible.

Output: ZPL to the Zebra QLn420 over Wi-Fi, plus a PNG “second copy”.

Quality of life

Preview now renders as a true 4×4″ square (matches the printed label).

Captions lean observational/philosophical rather than cryptic riddles.

Why I like it

SecondCopy treats misrecognition as material. The label is a tiny field report: context + place + a thinking error that occasionally hits the truth at an angle.

Next job — Mode B (Relay to Ezra)

I'm turning this from a private tool into a public submission app:

Users create as in Mode A.

Instead of printing locally, they tap Relay to Ezra.

The app uploads PNG + ZPL + metadata to a small cloud inbox (Firebase).

A home listener watches the queue and auto-prints to the Misrecogniser in the studio.

The user still keeps a “second copy” in Photos.

Thank you for visiting my gallery. I would be immensely grateful for your support. You can find many of my other creations here:

patreon.com/LewdNet799

AI technology has come a long way in recent years, and one area where it has made significant progress is in image captioning. Image captioning refers to the process of generating a textual description of an image or video. In this article, we will explore how AI technology works with captioning and the different approaches used to generate captions.

Neural Networks

Neural networks are a key technology in image captioning. These networks are designed to mimic the human brain and can learn from examples and data. The networks consist of several layers of nodes, each of which performs a specific operation on the input data. For image captioning, the neural network is trained on a large dataset of images and their associated captions. The network then uses this training to generate captions for new images.

Natural Language Processing

Natural language processing (NLP) is another important technology used in image captioning. NLP is a subfield of AI that focuses on the interaction between computers and human language. It involves the analysis of language and the development of algorithms that can understand and generate natural language. In image captioning, NLP is used to generate captions that are grammatically correct and semantically meaningful.

Attention Mechanism

Attention mechanism is a technique used to improve the performance of neural networks in image captioning. It works by allowing the network to focus its attention on specific parts of the image when generating the caption. For example, if the image contains a person, the attention mechanism can direct the network to focus on the person's face when generating the caption. This helps to ensure that the generated caption is more accurate and relevant to the image.

Transfer Learning

Transfer learning is a technique that involves using a pre-trained neural network as a starting point for a new task. In image captioning, transfer learning can be used to improve the performance of the network by starting with a pre-trained network that has already been trained on a large dataset of images and captions. This allows the network to learn more quickly and accurately, reducing the amount of training time required.